[Python for data analysis] - Diabetes Dataset

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Introduction

This dataset represents 10 years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks. It includes over 50 features representing patient and hospital outcomes. Information was extracted from the database for encounters that satisfied the following criteria.

• (1) It is an inpatient encounter (a hospital admission).
• (2) It is a diabetic encounter, that is, one during which any kind of diabetes was entered to the system as a diagnosis.
• (3) The length of stay was at least 1 day and at most 14 days.
• (4) Laboratory tests were performed during the encounter.
• (5) Medications were administered during the encounter.

The data contains such attributes as patient number, race, gender, age, admission type, time in hospital, medical specialty of admitting physician, number of lab test performed, HbA1c test result, diagnosis, number of medication, diabetic medications, number of outpatient, inpatient, and emergency visits in the year before the hospitalization, etc.

Dataset can be found at https://archive.ics.uci.edu/ml/datasets/diabetes+130-us+hospitals+for+years+1999-2008

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Table Of Contents

• Introduction

• Table Of Contents

• Data Exploration

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Table Of Contents

• Introduction
• Table Of Contents
• Data Exploration
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  • Dataset
  • Overview
  • Columns signification
  • Profiling report
  • Data cleaning
  • Rethink columns
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• Data Visualization

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  • Data Visualization
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    • Correlation Matrix
    • Boxplots
    • Distributions
    • Deeper analysis
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  • Machine Learning

    • Case 1 : Predict patient's readmission under 30 days

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      • Principal Component Analysis
      • Machine Learning
        • Case 1 : Predict patient's readmission under 30 days
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          • Preparing data
            • Building of the Machine Learning oriented dataset
            • Dealing with the column to predict (Predict patient's readmission under 30 days)
            • Dealing with categorical features
            • Shuffling
            • Splitting
            • Scaling
          • Fitting models
            • Model 1: K-Nearest Neighbors (KNN)
            • Model 2: Logistic regression
            • Model 3: Linear SVC
            • Model 4: Random Forest
            • Model 5: Adaptive boosting
            • Model 6: Decision Tree
            • Model 7: ExtraTrees
            • Model 8: Naive Bayes Classifier
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          • Tuning models
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          • Comparing models
            • Performance
            • Barplot
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        • Case 2 : Predict patient's readmission below and above 30 days

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          • Case 2 : Predict patient's readmission below and above 30 days
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            • Dealing with the column to predict (Predict patient's readmission below AND above 30 days)
              • Model 1 : K-Nearest Neighbors (KNN) version 2
              • Model 2 : Logistic Regression version 2
              • Model 3 : Linear SVC
              • Model 4 : Random Forest
              • Model 5 : Adaptive boosting
              • Model 6 : Decision Tree
              • Model 7 : ExtraTrees
              • Model 8 : Naive Bayes Classifier
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            • Comparing all models
              • Performance
              • Barplot
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              • Tuning models
              • Comparing models
                • Performance
                • Barplot
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            • Remarks about cases

          • Conclusion

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        • Conclusion
          • Remarks about cases
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Data Exploration

Importation of all the librairies that we will use

import numpy as np
import pandas as pd
from pandas_profiling import ProfileReport
import matplotlib.pyplot as plt
import seaborn as sns
from bokeh.plotting import figure, output_file, show
from bokeh.io import output_notebook, show
from bokeh.layouts import gridplot
import pandas_bokeh
from plotly.subplots import make_subplots
from plotly import tools
import plotly.offline as py
import plotly.graph_objs as go
import plotly.express as px 
import random
from tabulate import tabulate

Random State

We define a seed to have reproducible results.

np.random.seed(77877)

Display settings

We set the bokeh output to notebook

output_notebook()

Loading BokehJS ...

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We define a permanent style for the plot in order to have an homogenous vizualisation

plt.style.use('default')
from bokeh.io import curdoc
curdoc().theme = 'dark_minimal'

All bokeh and plotly graphs are dynamic, so you can move the mouse over an element to see which data he refers to.
You can also interact with the legend of the plot to show/hide some categories

%matplotlib inline

We set the maximum number of columns allowed to be displayed to 100 so that the all dataframe can be printed

pd.set_option('display.max_columns', 100)

Importation of the dataset

We create a dataframe by reading a csv file and importing the data

diabetes_df = pd.read_csv(r'../Dataset/diabetic_data.csv', sep =',', na_values="?", low_memory = False)
diabetes_df.head()

	encounter_id	patient_nbr	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	medical_specialty	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	diag_1	diag_2	diag_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide-metformin	glipizide-metformin	glimepiride-pioglitazone	metformin-rosiglitazone	metformin-pioglitazone	change	diabetesMed	readmitted
0	2278392	8222157	Caucasian	Female	[0-10)	NaN	6	25	1	1	NaN	Pediatrics-Endocrinology	41	0	1	0	0	0	250.83	NaN	NaN	1	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	NO
1	149190	55629189	Caucasian	Female	[10-20)	NaN	1	1	7	3	NaN	NaN	59	0	18	0	0	0	276	250.01	255	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Up	No	No	No	No	No	Ch	Yes	>30
2	64410	86047875	AfricanAmerican	Female	[20-30)	NaN	1	1	7	2	NaN	NaN	11	5	13	2	0	1	648	250	V27	6	None	None	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	NO
3	500364	82442376	Caucasian	Male	[30-40)	NaN	1	1	7	2	NaN	NaN	44	1	16	0	0	0	8	250.43	403	7	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Up	No	No	No	No	No	Ch	Yes	NO
4	16680	42519267	Caucasian	Male	[40-50)	NaN	1	1	7	1	NaN	NaN	51	0	8	0	0	0	197	157	250	5	None	None	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	Ch	Yes	NO

diabetes_df.tail()

	encounter_id	patient_nbr	race	gender	age	weight	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	payer_code	medical_specialty	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	diag_1	diag_2	diag_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	examide	citoglipton	insulin	glyburide-metformin	glipizide-metformin	glimepiride-pioglitazone	metformin-rosiglitazone	metformin-pioglitazone	change	diabetesMed	readmitted
101761	443847548	100162476	AfricanAmerican	Male	[70-80)	NaN	1	3	7	3	MC	NaN	51	0	16	0	0	0	250.13	291	458	9	None	>8	Steady	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Down	No	No	No	No	No	Ch	Yes	>30
101762	443847782	74694222	AfricanAmerican	Female	[80-90)	NaN	1	4	5	5	MC	NaN	33	3	18	0	0	1	560	276	787	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	No	No	No	Yes	NO
101763	443854148	41088789	Caucasian	Male	[70-80)	NaN	1	1	7	1	MC	NaN	53	0	9	1	0	0	38	590	296	13	None	None	Steady	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Down	No	No	No	No	No	Ch	Yes	NO
101764	443857166	31693671	Caucasian	Female	[80-90)	NaN	2	3	7	10	MC	Surgery-General	45	2	21	0	0	1	996	285	998	9	None	None	No	No	No	No	No	No	Steady	No	No	Steady	No	No	No	No	No	No	No	Up	No	No	No	No	No	Ch	Yes	NO
101765	443867222	175429310	Caucasian	Male	[70-80)	NaN	1	1	7	6	NaN	NaN	13	3	3	0	0	0	530	530	787	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	NO

By fisrt obersving the data, we can see that there are a lot of values in this dataset. There are 101 766 row and 50 columns. We will certainly have to analyze and clean up these data a little to reduce them and make them more treatable.

First overview

Let's use the info() method in order to observe the structure and the type of the dataframe

 diabetes_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 101766 entries, 0 to 101765
Data columns (total 50 columns):
 #   Column                    Non-Null Count   Dtype 
---  ------                    --------------   ----- 
 0   encounter_id              101766 non-null  int64 
 1   patient_nbr               101766 non-null  int64 
 2   race                      99493 non-null   object
 3   gender                    101766 non-null  object
 4   age                       101766 non-null  object
 5   weight                    3197 non-null    object
 6   admission_type_id         101766 non-null  int64 
 7   discharge_disposition_id  101766 non-null  int64 
 8   admission_source_id       101766 non-null  int64 
 9   time_in_hospital          101766 non-null  int64 
 10  payer_code                61510 non-null   object
 11  medical_specialty         51817 non-null   object
 12  num_lab_procedures        101766 non-null  int64 
 13  num_procedures            101766 non-null  int64 
 14  num_medications           101766 non-null  int64 
 15  number_outpatient         101766 non-null  int64 
 16  number_emergency          101766 non-null  int64 
 17  number_inpatient          101766 non-null  int64 
 18  diag_1                    101745 non-null  object
 19  diag_2                    101408 non-null  object
 20  diag_3                    100343 non-null  object
 21  number_diagnoses          101766 non-null  int64 
 22  max_glu_serum             101766 non-null  object
 23  A1Cresult                 101766 non-null  object
 24  metformin                 101766 non-null  object
 25  repaglinide               101766 non-null  object
 26  nateglinide               101766 non-null  object
 27  chlorpropamide            101766 non-null  object
 28  glimepiride               101766 non-null  object
 29  acetohexamide             101766 non-null  object
 30  glipizide                 101766 non-null  object
 31  glyburide                 101766 non-null  object
 32  tolbutamide               101766 non-null  object
 33  pioglitazone              101766 non-null  object
 34  rosiglitazone             101766 non-null  object
 35  acarbose                  101766 non-null  object
 36  miglitol                  101766 non-null  object
 37  troglitazone              101766 non-null  object
 38  tolazamide                101766 non-null  object
 39  examide                   101766 non-null  object
 40  citoglipton               101766 non-null  object
 41  insulin                   101766 non-null  object
 42  glyburide-metformin       101766 non-null  object
 43  glipizide-metformin       101766 non-null  object
 44  glimepiride-pioglitazone  101766 non-null  object
 45  metformin-rosiglitazone   101766 non-null  object
 46  metformin-pioglitazone    101766 non-null  object
 47  change                    101766 non-null  object
 48  diabetesMed               101766 non-null  object
 49  readmitted                101766 non-null  object
dtypes: int64(13), object(37)
memory usage: 38.8+ MB

diabetes_df.describe().apply(lambda s: s.apply('{0:.2f}'.format))

	encounter_id	patient_nbr	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	number_diagnoses
count	101766.00	101766.00	101766.00	101766.00	101766.00	101766.00	101766.00	101766.00	101766.00	101766.00	101766.00	101766.00	101766.00
mean	165201645.62	54330400.69	2.02	3.72	5.75	4.40	43.10	1.34	16.02	0.37	0.20	0.64	7.42
std	102640295.98	38696359.35	1.45	5.28	4.06	2.99	19.67	1.71	8.13	1.27	0.93	1.26	1.93
min	12522.00	135.00	1.00	1.00	1.00	1.00	1.00	0.00	1.00	0.00	0.00	0.00	1.00
25%	84961194.00	23413221.00	1.00	1.00	1.00	2.00	31.00	0.00	10.00	0.00	0.00	0.00	6.00
50%	152388987.00	45505143.00	1.00	1.00	7.00	4.00	44.00	1.00	15.00	0.00	0.00	0.00	8.00
75%	230270887.50	87545949.75	3.00	4.00	7.00	6.00	57.00	2.00	20.00	0.00	0.00	1.00	9.00
max	443867222.00	189502619.00	8.00	28.00	25.00	14.00	132.00	6.00	81.00	42.00	76.00	21.00	16.00

Columns signification

Now we want to know and understand the signification of the differents columns in order to analyse them. With this dataset, we have a pdf document explaining some characteristics of these data:

• Feature name Description and values
• Encounter ID Unique identifier of an encounter
• Patient number Unique identifier of a patient
• Race Values: Caucasian, Asian, African American, Hispanic, and other
• Gender Values: male, female, and unknown/invalid
• Age Grouped in 10-year intervals: [0, 10), [10, 20), . . ., [90, 100)
• Weight Weight in pounds
• Admission type Integer identifier corresponding to 9 distinct values, for example, emergency, urgent,elective, newborn, and not available
• Discharge disposition Integer identifier corresponding to 29 distinct values, for example, discharged to home, expired, and not available
• Admission source Integer identifier corresponding to 21 distinct values, for example, physician referral, emergency room, and transfer from a hospital
• Time in hospital Integer number of days between admission and discharge
• Payer code Integer identifier corresponding to 23 distinct values, for example, Blue Cross\Blue, Shield, Medicare, and self-pay
• Medical specialty Integer identifier of a specialty of the admitting physician, corresponding to 84 distinct values, for example, cardiology, internal medicine, family\general practice, and surgeon
• Number of lab procedures Number of lab tests performed during the encount
• Number of procedures Number of procedures (other than lab tests) performed during the encounter
• Number of medications Number of distinct generic names administered during the encounter
• Number of outpatient visits Number of outpatient visits of the patient in the year preceding the encounter
• Number of emergency visits Number of emergency visits of the patient in the year preceding the encounter
• Number of inpatient visits Number of inpatient visits of the patient in the year preceding the encounter
• Diagnosis 1 "The primary diagnosis (coded as first three digits of ICD9)** 848 distinct values"
• Diagnosis 2 "Secondary diagnosis (coded as first three digits of ICD9)** 923 distinct values"
• Diagnosis 3 "Additional secondary diagnosis (coded as first three digits of ICD9)** 954 distinct values"
• Number of diagnoses Number of diagnoses entered to the system
• Glucose serum test result Indicates the range of the result or if the test was not taken. (“>200,” “>300, “normal,” and “none” if not measured)
• A1c test result Indicates the range of the result or if the test was not taken. Values: “>8” if the result was greater than 8%, “>7” if the result was greater than 7% but less than 8%, “normal” if the result was less than 7%, and “none” if not measu
• Change of medications Indicates if there was a change in diabetic medications (either dosage or generic name). Values: “change” and “no change”
• Diabetes medications Indicates if there was any diabetic medication prescribed. Values: “yes” and “no”
• 24 features for medications For the generic names: metformin, repaglinide, nateglinide, chlorpropamide,glimepiride, acetohexamide, glipizide, glyburide, tolbutamide, pioglitazone,rosiglitazone,acarbose, miglitol, troglitazone, tolazamide, examide, - sitagliptin, insulin,glyburide-metformin, glipizide-metformin, glimepiride-pioglitazone,metformin-rosiglitazone, and metformin-pioglitazone, the feature indicates whetherthe drug was prescribed or there was a change in the dosage. Values:“up” if the dosage - was increased during the encounter, “down” if the dosage was decreased, “steady” if the dosage did not change, and “no” if the drug was not prescri
• Readmitted Days to inpatient readmission. Values: “<30” if the patient was readmitted in less than 30 days, “>30” if the patient was readmitted in more than 30 days, and “No” for no record of readmission.-

Here, we will use the pandas_profiling library to call the profil_report() function. This library extends the DataFrame for a quick analysis

This report contains several informations such as the types of columns in the dataframe, the uniques and missing values, it will do some quantile and descriptive statistics, will search some correlations between the differents features. We will use it as a base to clean up our data.

Profile report

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report = ProfileReport(diabetes_df, title = "Diabetes dataset overview", dark_mode = True)
report
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#report = ProfileReport(diabetes_df, title = "Diabetes dataset overview", dark_mode = True)
#report
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report = ProfileReport(diabetes_df, title = "Diabetes dataset overview", dark_mode = True)
report
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Data cleaning

Counting unknown values

Here we count the pourcentage of missing value in the differents columns of this dataframe

missing_values = diabetes_df.isna().sum() / len(diabetes_df) * 100
missing_values = missing_values[missing_values > 0]
missing_values.sort_values(inplace=True)
missing_values = missing_values.to_frame()
missing_values

	0
diag_1	0.020636
diag_2	0.351787
diag_3	1.398306
race	2.233555
payer_code	39.557416
medical_specialty	49.082208
weight	96.858479

Dropping columns with too much missing values

With the precedent results, we eliminate the columns that have too many missing values

diabetes_df.drop(columns = ['weight', 'medical_specialty', 'payer_code', 'encounter_id'], inplace = True)

Some columns provide a constant value for every row, it is useless so we can eliminate them too.

diabetes_df.drop(columns = ['examide', 'citoglipton', 'glimepiride-pioglitazone', 'metformin-rosiglitazone', 'metformin-pioglitazone'], inplace = True)

Dropping redundant rows

When we analysed some data in this dataset, we observed that there was some redundant rows, some rows had the same number of patient value, and sometimes the linked variables were not coherent

We also drop these rows

diabetes_df.drop_duplicates(subset = "patient_nbr", keep = 'first', inplace = True)

Because of the deletion of redundant rows, some index were erased, we need to reset index

diabetes_df.reset_index(inplace = True, drop = True)
diabetes_df

	patient_nbr	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	diag_1	diag_2	diag_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	insulin	glyburide-metformin	glipizide-metformin	change	diabetesMed	readmitted
0	8222157	Caucasian	Female	[0-10)	6	25	1	1	41	0	1	0	0	0	250.83	NaN	NaN	1	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	NO
1	55629189	Caucasian	Female	[10-20)	1	1	7	3	59	0	18	0	0	0	276	250.01	255	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Up	No	No	Ch	Yes	>30
2	86047875	AfricanAmerican	Female	[20-30)	1	1	7	2	11	5	13	2	0	1	648	250	V27	6	None	None	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	No	No	Yes	NO
3	82442376	Caucasian	Male	[30-40)	1	1	7	2	44	1	16	0	0	0	8	250.43	403	7	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Up	No	No	Ch	Yes	NO
4	42519267	Caucasian	Male	[40-50)	1	1	7	1	51	0	8	0	0	0	197	157	250	5	None	None	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	Steady	No	No	Ch	Yes	NO
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
71513	183087545	Caucasian	Female	[70-80)	1	1	7	9	50	2	33	0	0	0	574	574	250.02	9	None	>7	No	No	No	No	No	No	No	Up	No	No	No	No	No	No	No	Steady	No	No	Ch	Yes	>30
71514	188574944	Other	Female	[40-50)	1	1	7	14	73	6	26	0	1	0	592	599	518	9	None	>8	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	Up	No	No	Ch	Yes	>30
71515	140199494	Other	Female	[60-70)	1	1	7	2	46	6	17	1	1	1	996	585	403	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	Yes	>30
71516	120975314	Caucasian	Female	[80-90)	1	1	7	5	76	1	22	0	1	0	292	8	304	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Up	No	No	Ch	Yes	NO
71517	175429310	Caucasian	Male	[70-80)	1	1	7	6	13	3	3	0	0	0	530	530	787	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	NO

71518 rows × 41 columns

Rethink some colums format

Diag_1, Diag_2, Diag_3: mapping ICD9 codes with corresponding names

https://www.hindawi.com/journals/bmri/2014/781670/tab2/

We create a dictionnary in order to map the value later, this dictionnary correspond to the values found in the link above

map_diag_123 = {'Circulatory' : [x for x in range(390,460)]+[785],
                'Respiratory' : [x for x in range(460,519)]+[786],
                'Digestive' : [x for x in range(520,580)]+[787],
                'Diabetes' : list(set(np.linspace(250, 251, 101)) - set([251])),
                'Injury' : [x for x in range(800,1000)],
                'Musculoskeletal' : [x for x in range(710,440)],
                'Genitourinary' : [x for x in range(580,630)]+[788],
                'Neoplasms' : list(set([x for x in range(1,280)] + [780, 781, 782, 784] + [x for x in range(790, 799)] + [x for x in range(680, 709)] + [x for x in range(290, 320)]) - set([250]))}

This mapping method replace all the value by their corresponding in order to have traitable data. The numeric values in the column diag1, diag2, diag3 were too scattered, we had to regroup them by using the informations in the link above

def mapping_df(diabetes_df, column):
    #for the missing value
    if diabetes_df[column] is np.NaN :
        return "No data"
    value = str(diabetes_df[column])
    #there are some value that begin with a character so we treat them first to avoid errors
    if value[0] == "E" : 
        return "Other"
    elif value[0] == "V" : 
        return "Other"
    #we convert the others value to float to have a correct conversion 
    else : value = float(diabetes_df[column])
        
    #now each float value is analysed and in fonction of her category, a string value will be assigned to her
    if value in [x for x in range(390,460)]+[785] : return "Circulatory"
    elif value in [x for x in range(460,519)]+[786] : return "Respiratory"
    elif value in [x for x in range(520,580)]+[787] : return "Digestive"
    elif value in list(set(np.linspace(250, 251, 101)) - set([251])) : return "Diabetes"
    elif value in [x for x in range(800,1000)] : return "Injury"
    elif value in [x for x in range(710,740)] : return "Musculoskeletal"
    elif value in [x for x in range(580,630)]+[788] : return "Genitourinary"
    elif value in list(set([x for x in range(1,280)] + [780, 781, 782, 784] + [x for x in range(790, 799)] + [x for x in range(680, 709)] + [x for x in range(290, 320)]) - set([250])) : return "Neoplasms"
    else : return "Other"

Now we apply the mapping method to the three differents columns

diabetes_df['diag_1'] = diabetes_df.apply(mapping_df, axis = 1, column = 'diag_1')
diabetes_df['diag_2'] = diabetes_df.apply(mapping_df, axis = 1, column = 'diag_2')
diabetes_df['diag_3'] = diabetes_df.apply(mapping_df, axis = 1, column = 'diag_3')

Now we want to create new columns to interpret the differents diagnosis of the patient, to do so, we first create a method that will give boolean value for the existence of the different diagnosis

def value_exists(column1, column2, column3, diagnosis):
    value = []
    for i in range(len(column1)):
        if str(column1[i]) == diagnosis or str(column2[i]) == diagnosis or str(column3[i]) == diagnosis:
            #if the correspondant diagnosis is present
            value.append(True)
        else : value.append(False)
    return value

Now we map this value into new columns to interpret later with graphs and learning models

for diag in map_diag_123.keys():
    name = "Diag_" + str(diag)
    diabetes_df[name] = pd.Series(value_exists(diabetes_df['diag_1'], 
                                               diabetes_df['diag_2'], 
                                               diabetes_df['diag_3'], diag))

Rethink some other columns

We want to see what are the actual differents names of the columns of this dataset

diabetes_df.columns

Index(['patient_nbr', 'race', 'gender', 'age', 'admission_type_id',
       'discharge_disposition_id', 'admission_source_id', 'time_in_hospital',
       'num_lab_procedures', 'num_procedures', 'num_medications',
       'number_outpatient', 'number_emergency', 'number_inpatient', 'diag_1',
       'diag_2', 'diag_3', 'number_diagnoses', 'max_glu_serum', 'A1Cresult',
       'metformin', 'repaglinide', 'nateglinide', 'chlorpropamide',
       'glimepiride', 'acetohexamide', 'glipizide', 'glyburide', 'tolbutamide',
       'pioglitazone', 'rosiglitazone', 'acarbose', 'miglitol', 'troglitazone',
       'tolazamide', 'insulin', 'glyburide-metformin', 'glipizide-metformin',
       'change', 'diabetesMed', 'readmitted', 'Diag_Circulatory',
       'Diag_Respiratory', 'Diag_Digestive', 'Diag_Diabetes', 'Diag_Injury',
       'Diag_Musculoskeletal', 'Diag_Genitourinary', 'Diag_Neoplasms'],
      dtype='object')

We can map another columns to have a better idea of the different admission type in the later graphs. We use here the mapping values gave in a document with the dataset

map_admission_type_id = {1: 'Emergency',
                         2:'Urgent',
                         3:'Elective',
                         4:'Newborn',
                         5:'Not Available',
                         6:'NULL',
                         7:'Trauma Center',
                         8 :'Not Mapped'}

diabetes_df.admission_type_id = diabetes_df.admission_type_id.map(map_admission_type_id)

map_discharge_disposition_id = {1:"Discharged to home",
                                2:"Discharged/transferred to another short term hospital",
                                3:"Discharged/transferred to SNF",
                                4:"Discharged/transferred to ICF",
                                5:"Discharged/transferred to another type of inpatient care institution",
                                6:"Discharged/transferred to home with home health service",
                                7:"Left AMA",
                                8:"Discharged/transferred to home under care of Home IV provider",
                                9:"Admitted as an inpatient to this hospital",
                                10:"Neonate discharged to another hospital for neonatal aftercare",
                                11:"Expired",
                                12:"Still patient or expected to return for outpatient services",
                                13:"Hospice / home",
                                14:"Hospice / medical facility",
                                15:"Discharged/transferred within this institution to Medicare approved swing bed",
                                16:"Discharged/transferred/referred another institution for outpatient services",
                                17:"Discharged/transferred/referred to this institution for outpatient services",
                                18:"NULL",
                                19:"Expired at home. Medicaid only, hospice.",
                                20:"Expired in a medical facility. Medicaid only, hospice.",
                                21:"Expired, place unknown. Medicaid only, hospice.",
                                22:"Discharged/transferred to another rehab fac including rehab units of a hospital .",
                                23:"Discharged/transferred to a long term care hospital.",
                                24:"Discharged/transferred to a nursing facility certified under Medicaid but not certified under Medicare.",
                                25:"Not Mapped",
                                26:"Unknown/Invalid",
                                30:"Discharged/transferred to another Type of Health Care Institution not Defined Elsewhere",
                                27:"Discharged/transferred to a federal health care facility.",
                                28:"Discharged/transferred/referred to a psychiatric hospital of psychiatric distinct part unit of a hospital",
                                29:"Discharged/transferred to a Critical Access Hospital (CAH)."}

diabetes_df.discharge_disposition_id = diabetes_df.discharge_disposition_id.map(map_discharge_disposition_id)

map_admission_source_id = {1:"Physician Referral",
                           2:"Clinic Referral",
                           3:"HMO Referral",
                           4:"Transfer from a hospital",
                           5:"Transfer from a Skilled Nursing Facility (SNF)",
                           6:"Transfer from another health care facility",
                           7:"Emergency Room",
                           8:"Court/Law Enforcement",
                           9:"Not Available",
                           10:"Transfer from critial access hospital",
                           11:"Normal Delivery",
                           12:"Premature Delivery",
                           13:"Sick Baby",
                           14:"Extramural Birth",
                           15:"Not Available",
                           17:"NULL",
                           18:"Transfer From Another Home Health Agency",
                           19:"Readmission to Same Home Health Agency",
                           20:"Not Mapped",
                           21:"Unknown/Invalid",
                           22:"Transfer from hospital inpt/same fac reslt in a sep claim",
                           23:"Born inside this hospital",
                           24:"Born outside this hospital",
                           25:"Transfer from Ambulatory Surgery Center",
                           26:"Transfer from Hospice"}

diabetes_df.admission_source_id = diabetes_df.admission_source_id.map(map_admission_source_id)

Now that the dataset has been cleaned, we can visualize all transformations and save it to a csv file

diabetes_df

	patient_nbr	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	diag_1	diag_2	diag_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	insulin	glyburide-metformin	glipizide-metformin	change	diabetesMed	readmitted	Diag_Circulatory	Diag_Respiratory	Diag_Digestive	Diag_Diabetes	Diag_Injury	Diag_Musculoskeletal	Diag_Genitourinary	Diag_Neoplasms
0	8222157	Caucasian	Female	[0-10)	NULL	Not Mapped	Physician Referral	1	41	0	1	0	0	0	Diabetes	No data	No data	1	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	NO	False	False	False	True	False	False	False	False
1	55629189	Caucasian	Female	[10-20)	Emergency	Discharged to home	Emergency Room	3	59	0	18	0	0	0	Neoplasms	Diabetes	Neoplasms	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Up	No	No	Ch	Yes	>30	False	False	False	True	False	False	False	True
2	86047875	AfricanAmerican	Female	[20-30)	Emergency	Discharged to home	Emergency Room	2	11	5	13	2	0	1	Other	Diabetes	Other	6	None	None	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	No	No	No	No	Yes	NO	False	False	False	True	False	False	False	False
3	82442376	Caucasian	Male	[30-40)	Emergency	Discharged to home	Emergency Room	2	44	1	16	0	0	0	Neoplasms	Diabetes	Circulatory	7	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Up	No	No	Ch	Yes	NO	True	False	False	True	False	False	False	True
4	42519267	Caucasian	Male	[40-50)	Emergency	Discharged to home	Emergency Room	1	51	0	8	0	0	0	Neoplasms	Neoplasms	Diabetes	5	None	None	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	Steady	No	No	Ch	Yes	NO	False	False	False	True	False	False	False	True
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
71513	183087545	Caucasian	Female	[70-80)	Emergency	Discharged to home	Emergency Room	9	50	2	33	0	0	0	Digestive	Digestive	Diabetes	9	None	>7	No	No	No	No	No	No	No	Up	No	No	No	No	No	No	No	Steady	No	No	Ch	Yes	>30	False	False	True	True	False	False	False	False
71514	188574944	Other	Female	[40-50)	Emergency	Discharged to home	Emergency Room	14	73	6	26	0	1	0	Genitourinary	Genitourinary	Respiratory	9	None	>8	No	No	No	No	No	No	Steady	No	No	No	No	No	No	No	No	Up	No	No	Ch	Yes	>30	False	True	False	False	False	False	True	False
71515	140199494	Other	Female	[60-70)	Emergency	Discharged to home	Emergency Room	2	46	6	17	1	1	1	Injury	Genitourinary	Circulatory	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Steady	No	No	No	Yes	>30	True	False	False	False	True	False	True	False
71516	120975314	Caucasian	Female	[80-90)	Emergency	Discharged to home	Emergency Room	5	76	1	22	0	1	0	Neoplasms	Neoplasms	Neoplasms	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Up	No	No	Ch	Yes	NO	False	False	False	False	False	False	False	True
71517	175429310	Caucasian	Male	[70-80)	Emergency	Discharged to home	Emergency Room	6	13	3	3	0	0	0	Digestive	Digestive	Digestive	9	None	None	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	NO	False	False	True	False	False	False	False	False

71518 rows × 49 columns

diabetes_df.to_csv(r'..\Dataset\diabetes_df.csv', sep = ';')

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>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

Data Visualization

Correlation Matrix

A correlation matrix is used to assess dependency between several variables at the sasme time. The result is a table containing the correlation coefficients between each variable and the other.

The closer the coefficient (between 0 and 1) is to 1, the more correlated are the two associated variables.

corr = diabetes_df.corr()
corr.style.background_gradient(cmap='coolwarm').set_precision(2)

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	patient_nbr	time_in_hospital	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	number_diagnoses	Diag_Circulatory	Diag_Respiratory	Diag_Digestive	Diag_Diabetes	Diag_Injury	Diag_Musculoskeletal	Diag_Genitourinary	Diag_Neoplasms
patient_nbr	1.00	-0.02	0.01	-0.02	0.02	0.09	0.06	0.09	0.23	-0.02	0.01	0.01	-0.10	0.02	0.00	0.05	0.02
time_in_hospital	-0.02	1.00	0.33	0.19	0.47	-0.01	-0.01	0.06	0.23	-0.03	0.02	0.01	-0.14	0.05	-0.02	0.05	0.05
num_lab_procedures	0.01	0.33	1.00	0.05	0.26	-0.01	0.01	0.08	0.16	-0.01	0.05	0.03	-0.07	-0.03	-0.12	0.10	0.04
num_procedures	-0.02	0.19	0.05	1.00	0.40	-0.02	-0.04	-0.02	0.09	0.15	-0.12	0.00	-0.10	0.08	0.02	-0.05	-0.16
num_medications	0.02	0.47	0.26	0.40	1.00	0.03	0.00	0.04	0.26	0.09	0.05	-0.05	-0.14	0.09	0.09	0.01	-0.11
number_outpatient	0.09	-0.01	-0.01	-0.02	0.03	1.00	0.10	0.07	0.08	-0.00	-0.01	-0.00	-0.03	-0.01	0.01	0.02	0.01
number_emergency	0.06	-0.01	0.01	-0.04	0.00	0.10	1.00	0.15	0.05	-0.03	0.01	0.01	0.01	-0.01	-0.00	0.01	0.03
number_inpatient	0.09	0.06	0.08	-0.02	0.04	0.07	0.15	1.00	0.07	0.01	0.00	-0.00	-0.03	0.01	-0.02	0.01	0.00
number_diagnoses	0.23	0.23	0.16	0.09	0.26	0.08	0.05	0.07	1.00	0.06	0.09	0.01	-0.42	0.07	-0.07	0.14	0.02
Diag_Circulatory	-0.02	-0.03	-0.01	0.15	0.09	-0.00	-0.03	0.01	0.06	1.00	-0.04	-0.19	-0.14	-0.12	-0.10	-0.13	-0.33
Diag_Respiratory	0.01	0.02	0.05	-0.12	0.05	-0.01	0.01	0.00	0.09	-0.04	1.00	-0.10	-0.12	-0.10	-0.09	-0.10	-0.12
Diag_Digestive	0.01	0.01	0.03	0.00	-0.05	-0.00	0.01	-0.00	0.01	-0.19	-0.10	1.00	-0.05	-0.07	-0.08	-0.05	-0.04
Diag_Diabetes	-0.10	-0.14	-0.07	-0.10	-0.14	-0.03	0.01	-0.03	-0.42	-0.14	-0.12	-0.05	1.00	-0.11	0.04	-0.13	-0.04
Diag_Injury	0.02	0.05	-0.03	0.08	0.09	-0.01	-0.01	0.01	0.07	-0.12	-0.10	-0.07	-0.11	1.00	-0.02	-0.03	-0.07
Diag_Musculoskeletal	0.00	-0.02	-0.12	0.02	0.09	0.01	-0.00	-0.02	-0.07	-0.10	-0.09	-0.08	0.04	-0.02	1.00	-0.06	-0.09
Diag_Genitourinary	0.05	0.05	0.10	-0.05	0.01	0.02	0.01	0.01	0.14	-0.13	-0.10	-0.05	-0.13	-0.03	-0.06	1.00	0.01
Diag_Neoplasms	0.02	0.05	0.04	-0.16	-0.11	0.01	0.03	0.00	0.02	-0.33	-0.12	-0.04	-0.04	-0.07	-0.09	0.01	1.00

<<<<<<< HEAD >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

	patient_nbr	time_in_hospital	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	number_diagnoses	Diag_Circulatory	Diag_Respiratory	Diag_Digestive	Diag_Diabetes	Diag_Injury	Diag_Musculoskeletal	Diag_Genitourinary	Diag_Neoplasms
patient_nbr	1.00	-0.02	0.01	-0.02	0.02	0.09	0.06	0.09	0.23	-0.02	0.01	0.01	-0.10	0.02	0.00	0.05	0.02
time_in_hospital	-0.02	1.00	0.33	0.19	0.47	-0.01	-0.01	0.06	0.23	-0.03	0.02	0.01	-0.14	0.05	-0.02	0.05	0.05
num_lab_procedures	0.01	0.33	1.00	0.05	0.26	-0.01	0.01	0.08	0.16	-0.01	0.05	0.03	-0.07	-0.03	-0.12	0.10	0.04
num_procedures	-0.02	0.19	0.05	1.00	0.40	-0.02	-0.04	-0.02	0.09	0.15	-0.12	0.00	-0.10	0.08	0.02	-0.05	-0.16
num_medications	0.02	0.47	0.26	0.40	1.00	0.03	0.00	0.04	0.26	0.09	0.05	-0.05	-0.14	0.09	0.09	0.01	-0.11
number_outpatient	0.09	-0.01	-0.01	-0.02	0.03	1.00	0.10	0.07	0.08	-0.00	-0.01	-0.00	-0.03	-0.01	0.01	0.02	0.01
number_emergency	0.06	-0.01	0.01	-0.04	0.00	0.10	1.00	0.15	0.05	-0.03	0.01	0.01	0.01	-0.01	-0.00	0.01	0.03
number_inpatient	0.09	0.06	0.08	-0.02	0.04	0.07	0.15	1.00	0.07	0.01	0.00	-0.00	-0.03	0.01	-0.02	0.01	0.00
number_diagnoses	0.23	0.23	0.16	0.09	0.26	0.08	0.05	0.07	1.00	0.06	0.09	0.01	-0.42	0.07	-0.07	0.14	0.02
Diag_Circulatory	-0.02	-0.03	-0.01	0.15	0.09	-0.00	-0.03	0.01	0.06	1.00	-0.04	-0.19	-0.14	-0.12	-0.10	-0.13	-0.33
Diag_Respiratory	0.01	0.02	0.05	-0.12	0.05	-0.01	0.01	0.00	0.09	-0.04	1.00	-0.10	-0.12	-0.10	-0.09	-0.10	-0.12
Diag_Digestive	0.01	0.01	0.03	0.00	-0.05	-0.00	0.01	-0.00	0.01	-0.19	-0.10	1.00	-0.05	-0.07	-0.08	-0.05	-0.04
Diag_Diabetes	-0.10	-0.14	-0.07	-0.10	-0.14	-0.03	0.01	-0.03	-0.42	-0.14	-0.12	-0.05	1.00	-0.11	0.04	-0.13	-0.04
Diag_Injury	0.02	0.05	-0.03	0.08	0.09	-0.01	-0.01	0.01	0.07	-0.12	-0.10	-0.07	-0.11	1.00	-0.02	-0.03	-0.07
Diag_Musculoskeletal	0.00	-0.02	-0.12	0.02	0.09	0.01	-0.00	-0.02	-0.07	-0.10	-0.09	-0.08	0.04	-0.02	1.00	-0.06	-0.09
Diag_Genitourinary	0.05	0.05	0.10	-0.05	0.01	0.02	0.01	0.01	0.14	-0.13	-0.10	-0.05	-0.13	-0.03	-0.06	1.00	0.01
Diag_Neoplasms	0.02	0.05	0.04	-0.16	-0.11	0.01	0.03	0.00	0.02	-0.33	-0.12	-0.04	-0.04	-0.07	-0.09	0.01	1.00	Diag_Neoplasms	0.02	0.05	0.04	-0.16	-0.11	0.01	0.03	0.00	0.02	-0.33	-0.12	-0.04	-0.04	-0.07	-0.09	0.01	1.00

Now we will use another library (seaborn) in order to plot the correlation matrix with another visual

#another style of correlation matrix with seaborn library 
plt.figure(figsize=(10, 5))
corrM = sns.heatmap(diabetes_df.corr(), cmap = 'Blues')
corrM.set_title('Correlation Heatmap', fontdict={'fontsize':20}, color = 'darkblue' );

By interpreting these correlations matrix, we can observe that this dataset does not present many correlations between its different variables. The coefficients are relatively low and the highest ones remain small coefficients.

The variables who seem to have a high correlation are :

• num_lab_procedures, num_procedures and num_medications with time_in_hospital
• num_medications with num_lab_procedures
• num_medications with num_procedures
• number_diagnoses with time_in_hospital and num_lab_procedures and Diag_Diabetes
• Diag_Neoplams with Diag_Circulatory
• Diag_Digestive with Diag_Circulatory

diabetes_df.columns.values

array(['patient_nbr', 'race', 'gender', 'age', 'admission_type_id',
       'discharge_disposition_id', 'admission_source_id',
       'time_in_hospital', 'num_lab_procedures', 'num_procedures',
       'num_medications', 'number_outpatient', 'number_emergency',
       'number_inpatient', 'diag_1', 'diag_2', 'diag_3',
       'number_diagnoses', 'max_glu_serum', 'A1Cresult', 'metformin',
       'repaglinide', 'nateglinide', 'chlorpropamide', 'glimepiride',
       'acetohexamide', 'glipizide', 'glyburide', 'tolbutamide',
       'pioglitazone', 'rosiglitazone', 'acarbose', 'miglitol',
       'troglitazone', 'tolazamide', 'insulin', 'glyburide-metformin',
       'glipizide-metformin', 'change', 'diabetesMed', 'readmitted',
       'Diag_Circulatory', 'Diag_Respiratory', 'Diag_Digestive',
       'Diag_Diabetes', 'Diag_Injury', 'Diag_Musculoskeletal',
       'Diag_Genitourinary', 'Diag_Neoplasms'], dtype=object)

Boxplots

Now, using the plotly library, we can plot some boxplots.

We select the different columns relevent to boxplot

columns = ['age','time_in_hospital', 'num_lab_procedures','num_procedures','num_medications','number_diagnoses']

Let's put some colors on that notebook : )

colors = ['dodgerblue', 'green', 'yellow', 'white', 'crimson', 'darkmagenta']

fig = make_subplots(rows = 2, cols=3, subplot_titles = columns)
i = 0

for column in columns:
    figure = px.box(diabetes_df, y = column, notched=True)
    figure.update_traces(fillcolor=colors[i], opacity=0.8, marker_color = colors[i])
    
    for t in figure.data:
        fig.add_trace(t, row = i//3 + 1, col = i%3 + 1)
        
    i += 1
    
fig.layout.template = 'plotly_dark'
fig.update_layout(title_text='Boxplots')
fig.update_layout(width=930,height=930)
fig.show()

Distributions

Using bokeh, we can plot some graphs that will be more interesting with the preprocessing above
Each feature will first be analysed individually

Again, let's put some colors on that notebook : )

colors = ['dodgerblue', 'green', 'yellow', 'white', 'crimson']

We create a list of all columns that we want to visualize

columns = diabetes_df.columns.values.tolist()
columns.remove('patient_nbr') # No need to plot an unique identifier

plots = [] # list that will contain each bar plot
i = 0

for column in columns:
    p = diabetes_df[column].value_counts(normalize = True).sort_values(ascending=False)[0:5].plot_bokeh(kind="bar", 
                                                                                title = column.upper(), 
                                                                                line_color = colors[i], #color of the edge of the bars
                                                                                fill_color = colors[i], #color of the core of the bars
                                                                                fill_alpha = 0.6, #transparency of the core of the bars
                                                                                legend = False, #no need to display the legend, there is only one type of data
                                                                                show_figure=False)
    p.xaxis.axis_label = '' # hide x label
    p.yaxis.axis_label = '' # hide y label
    p.xaxis.major_label_orientation = np.pi/6 # rotate bar labels to improve visibility
    
    plots.append(p)
    i += 1 # to parse the colors list
    i = i%5 # to reset index in order to stay in the length of the list

show(gridplot(plots, ncols = 3, width=310, height=310))

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Deeper analysis

In this section, we will try to analyse links between features see the influence from one another.

diabetes_df.groupby('race', sort = False).readmitted.value_counts().unstack().plot_bokeh(
    kind="bar", 
    title = 'Readmission status of patients by race', 
    fill_alpha = 0.8)

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Figure(

id = '8243', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='8254', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='8257', ...), Grid(id='8261', ...), Legend(id='8294', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_x_scales = {},

extra_y_ranges = {},

extra_y_scales = {},

frame_height = None,

frame_width = None,

height = 400,

height_policy = 'auto',

hidpi = True,

inner_height = 0,

inner_width = 0,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LinearAxis(id='8258', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outer_height = 0,

outer_width = 0,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

renderers = [GlyphRenderer(id='8283', ...), GlyphRenderer(id='8303', ...), GlyphRenderer(id='8322', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

syncable = True,

tags = [],

title = Title(id='8244', ...),

title_location = 'above',

toolbar = Toolbar(id='8269', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = 600,

width_policy = 'auto',

x_range = DataRange1d(id='8246', ...),

x_scale = LinearScale(id='8250', ...),

y_range = DataRange1d(id='8248', ...),

y_scale = LinearScale(id='8252', ...))

diabetes_df[['age', 'readmitted']].value_counts(normalize = True).unstack().plot_bokeh(kind = 'bar')

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Figure(

id = '8809', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='8820', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='8823', ...), Grid(id='8827', ...), Legend(id='8860', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_x_scales = {},

extra_y_ranges = {},

extra_y_scales = {},

frame_height = None,

frame_width = None,

height = 400,

height_policy = 'auto',

hidpi = True,

inner_height = 0,

inner_width = 0,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LinearAxis(id='8824', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outer_height = 0,

outer_width = 0,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

renderers = [GlyphRenderer(id='8849', ...), GlyphRenderer(id='8869', ...), GlyphRenderer(id='8888', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

syncable = True,

tags = [],

title = Title(id='8810', ...),

title_location = 'above',

toolbar = Toolbar(id='8835', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = 600,

width_policy = 'auto',

x_range = DataRange1d(id='8812', ...),

x_scale = LinearScale(id='8816', ...),

y_range = DataRange1d(id='8814', ...),

y_scale = LinearScale(id='8818', ...))

diabetes_df.groupby('race').age.value_counts().unstack().plot_bokeh(
    kind= "bar", 
    title = 'Age of patients by race',
    fill_alpha = 0.8)

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=======

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Figure(

id = '9383', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='9394', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='9397', ...), Grid(id='9401', ...), Legend(id='9434', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_x_scales = {},

extra_y_ranges = {},

extra_y_scales = {},

frame_height = None,

frame_width = None,

height = 400,

height_policy = 'auto',

hidpi = True,

inner_height = 0,

inner_width = 0,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LinearAxis(id='9398', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outer_height = 0,

outer_width = 0,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

renderers = [GlyphRenderer(id='9423', ...), GlyphRenderer(id='9443', ...), GlyphRenderer(id='9462', ...), GlyphRenderer(id='9481', ...), GlyphRenderer(id='9500', ...), GlyphRenderer(id='9519', ...), GlyphRenderer(id='9538', ...), GlyphRenderer(id='9557', ...), GlyphRenderer(id='9576', ...), GlyphRenderer(id='9595', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

syncable = True,

tags = [],

title = Title(id='9384', ...),

title_location = 'above',

toolbar = Toolbar(id='9409', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = 600,

width_policy = 'auto',

x_range = DataRange1d(id='9386', ...),

x_scale = LinearScale(id='9390', ...),

y_range = DataRange1d(id='9388', ...),

y_scale = LinearScale(id='9392', ...))

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

from bokeh.models import Range1d
from bokeh.models import Span

mean_time_in_hospital = diabetes_df[['age', 'time_in_hospital']].groupby('age').mean().mean()

meanTH = Span(location = mean_time_in_hospital[0], dimension = 'width', line_color = 'white', line_width = 1)

p = diabetes_df[['age', 'time_in_hospital']].groupby('age').mean().plot_bokeh(
    kind = 'line',
    title = "Time in hospital (in days) in function of age",
    line_color = 'dodgerblue',
    legend = False,
    show_figure = False)

p.xaxis.axis_label = 'Age of patient' 
p.yaxis.axis_label = 'Time spent in hospital (in days)'

p.renderers.extend([meanTH])

show(p)

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=======

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We can see here that there is a relationship between the age of the patient and the time he spent in hospital. On average, the older patients spend more time in hospital than the youngers.

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mean_number_med = diabetes_df[['age', 'num_medications']].groupby('age').mean().mean()

meanNM = Span(location = mean_number_med[0], dimension = 'width', line_color = 'white', line_width = 1)

p = diabetes_df[['age', 'num_medications']].groupby('age').mean().plot_bokeh(
    kind = 'line', 
    title = "Number of distinct medications in function of age",
    line_color = 'crimson',
    legend = False,
    show_figure = False)

p.xaxis.axis_label = 'Age of patient' 
p.yaxis.axis_label = 'Distinct medications'

p.renderers.extend([meanNM])

show(p)

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=======

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee =======

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

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Here, the number of distinc medication seems to depend a lot on the patient's age with a peak for patients around 60-70 years old.
Once again, older patients seems to be more affected.

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p = diabetes_df[['age', 'diabetesMed']].value_counts(normalize = True).unstack().plot_bokeh(
    kind = 'bar',
    title = 'Diabetic medication prescribed (Yes/No) in function of age',
    fill_alpha = 0.6,
    show_figure = False)

p.xaxis.axis_label = 'Age of patient' 
p.yaxis.axis_label = 'Distinct medications'

show(p)

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=======

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee =======

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

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We can see that the Yes and No values follow the same pattern with many values for patients between 50 and 90 years old but this can simply be explained by the fact that the pattern follows the distribution of patient of this dataset.
But here we can deduce that for a class of age, almost 2 patient over 3 were given diabetic medication

fig = px.scatter_3d(data_frame=diabetes_df,
                    x='age',
                    y='time_in_hospital',
                    z='num_medications',
                    color='readmitted')

fig.layout.template = 'plotly_dark'
fig.update_layout(title_text='3D Scatter')
fig.update_traces(marker_size = 1)                                                    
                                                    
                                                    
fig.show()

Here, we can see that the green dots (the patients readmitted under 30 days seem to be regrouped at the center of the plot

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---

---

---

---

---

---

---

---

---

---

---

=======

---

Principal Component Analysis

from sklearn.decomposition import PCA pca = PCA() x_train_pca = pca.fit_transform(x_train) x_test_pca = pca.transform(x_test)colonnes = list(diabetes_df_ml.columns) colonnes.remove('readmitted')colors = ['red' if x == 1 else 'blue' for x in y_train] #markers = ['.' if x == 1 else '.' for x in y_train] pd.DataFrame(x_train_pca).plot(kind = 'scatter', x = 1, y = 0, c = colors, marker = ".",s = 2, figsize = (20,20))

Here, we tried to run a principal component analysis to find out which feature could explain the most the data
Unfortunately, we didn't come close to a good solution
We hoped that by plotting the dataset on the 2 major components and assigning them a color depending on the readmitted status, we would be able to discern a pattern but it was not the case.

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

---

Machine Learning

The aim of this section is to use the diabetes dataset to train some Machine Learning models using the diabete dataset in order to predict the readmission of a patient

We will therefore try to predict the value of the "readmitted" column to answer our question

The actual readmitted column contains 3 different values (No, >30, <30)

Later, we will rearrange this column in order to have a binary decision to predict

For all models, we will not have to define a random state because the Sckit Learn library uses the numpy random seed
which we defined at the very begining of this notebook.

Case 1 : Predict patient's readmission under 30 days

Preparation of the data to train and test the futurs models

In order to be able to use Machine Learning algorithms, we must have numerical values only.

Let's see which column is not numerical

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diabetes_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 71518 entries, 0 to 71517
Data columns (total 49 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   patient_nbr               71518 non-null  int64 
 1   race                      69570 non-null  object
 2   gender                    71518 non-null  object
 3   age                       71518 non-null  object
 4   admission_type_id         71518 non-null  object
 5   discharge_disposition_id  71518 non-null  object
 6   admission_source_id       71518 non-null  object
 7   time_in_hospital          71518 non-null  int64 
 8   num_lab_procedures        71518 non-null  int64 
 9   num_procedures            71518 non-null  int64 
 10  num_medications           71518 non-null  int64 
 11  number_outpatient         71518 non-null  int64 
 12  number_emergency          71518 non-null  int64 
 13  number_inpatient          71518 non-null  int64 
 14  diag_1                    71518 non-null  object
 15  diag_2                    71518 non-null  object
 16  diag_3                    71518 non-null  object
 17  number_diagnoses          71518 non-null  int64 
 18  max_glu_serum             71518 non-null  object
 19  A1Cresult                 71518 non-null  object
 20  metformin                 71518 non-null  object
 21  repaglinide               71518 non-null  object
 22  nateglinide               71518 non-null  object
 23  chlorpropamide            71518 non-null  object
 24  glimepiride               71518 non-null  object
 25  acetohexamide             71518 non-null  object
 26  glipizide                 71518 non-null  object
 27  glyburide                 71518 non-null  object
 28  tolbutamide               71518 non-null  object
 29  pioglitazone              71518 non-null  object
 30  rosiglitazone             71518 non-null  object
 31  acarbose                  71518 non-null  object
 32  miglitol                  71518 non-null  object
 33  troglitazone              71518 non-null  object
 34  tolazamide                71518 non-null  object
 35  insulin                   71518 non-null  object
 36  glyburide-metformin       71518 non-null  object
 37  glipizide-metformin       71518 non-null  object
 38  change                    71518 non-null  object
 39  diabetesMed               71518 non-null  object
 40  readmitted                71518 non-null  object
 41  Diag_Circulatory          71518 non-null  bool  
 42  Diag_Respiratory          71518 non-null  bool  
 43  Diag_Digestive            71518 non-null  bool  
 44  Diag_Diabetes             71518 non-null  bool  
 45  Diag_Injury               71518 non-null  bool  
 46  Diag_Musculoskeletal      71518 non-null  bool  
 47  Diag_Genitourinary        71518 non-null  bool  
 48  Diag_Neoplasms            71518 non-null  bool  
dtypes: bool(8), int64(9), object(32)
memory usage: 22.9+ MB

We can see that we have categorical (interpreted as object type here) and boolean features that needs to be transformed.

Let's begin with making a copy of our dataset

Building of the Machine Learning oriented dataset

To fit machine learning algorithms, we need to drop the patient nbr, this column cannot be used for training model Then, we create a copy of the actual dataframe that will be formatted to use Machine Learning algorithms

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

diabetes_df_ml = diabetes_df.drop(columns = ['patient_nbr']).copy()

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diabetes_df_ml.readmitted.value_counts(normalize = True).plot_bokeh(kind = 'bar',
                                                                    title = 'Readmitted', 
                                                                    line_color = 'crimson',
                                                                    fill_color = 'crimson', 
                                                                    fill_alpha = 0.6)

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=======

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee =======

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

<<<<<<< HEAD <<<<<<< HEAD

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

Figure(

id = '11718', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='11729', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='11732', ...), Grid(id='11736', ...), Legend(id='11769', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_x_scales = {},

extra_y_ranges = {},

extra_y_scales = {},

frame_height = None,

frame_width = None,

height = 400,

height_policy = 'auto',

hidpi = True,

inner_height = 0,

inner_width = 0,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LinearAxis(id='11733', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outer_height = 0,

outer_width = 0,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

renderers = [GlyphRenderer(id='11758', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

syncable = True,

tags = [],

title = Title(id='11719', ...),

title_location = 'above',

toolbar = Toolbar(id='11744', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = 600,

width_policy = 'auto',

x_range = DataRange1d(id='11721', ...),

x_scale = LinearScale(id='11725', ...),

y_range = DataRange1d(id='11723', ...),

y_scale = LinearScale(id='11727', ...))

Dealing with the column to predict - (Predict the patient readmission under 30 days)

As mentionned above, the actual readmission column contains 3 different values. So the idea is to map those values towards 0 or 1.

We will in first hand consider the question "Will the patient be readmitted under 30 days ?"

This involve the following change : False for the 'NO' and the '>30' value and True for the '<30' value.

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

map_readmitted = {"NO" : False,
                  ">30" : False,
                  "<30" : True}

diabetes_df_ml.readmitted = diabetes_df.readmitted.map(map_readmitted)

Dealing with categorical features

Now, the goal is to transform categorical features to numeric features We simply assign a number for each category of the feature To do so, we use the LabelEncoder from the Sklearn library.

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from sklearn.preprocessing import LabelEncoder

label_encoder = LabelEncoder()

diabetes_df_ml["race"] = label_encoder.fit_transform(diabetes_df_ml["race"].astype(str))
#diabetes_df_ml["readmitted"] = label_encoder.fit_transform(diabetes_df_ml["readmitted"].astype(str))

for column in diabetes_df_ml.select_dtypes(include=['object']).columns:
    diabetes_df_ml[column] = label_encoder.fit_transform(diabetes_df_ml[column])
    
for column in diabetes_df_ml.select_dtypes(include=['bool']).columns:
    diabetes_df_ml[column] = label_encoder.fit_transform(diabetes_df_ml[column])

We want now to observe the dataset to see if the transformation has been successful

We carefully verify each feature

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diabetes_df_ml[list(diabetes_df_ml.columns)[:10]]

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	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	num_lab_procedures	num_procedures	num_medications
0	2	0	0	2	24	9	1	41	0	1
1	2	0	1	1	1	2	3	59	0	18
2	0	0	2	1	1	2	2	11	5	13
3	2	1	3	1	1	2	2	44	1	16
4	2	1	4	1	1	2	1	51	0	8
...	...	...	...	...	...	...	...	...	...	...
71513	2	0	7	1	1	2	9	50	2	33
71514	4	0	4	1	1	2	14	73	6	26
71515	4	0	6	1	1	2	2	46	6	17
71516	2	0	8	1	1	2	5	76	1	22
71517	2	1	7	1	1	2	6	13	3	3

71518 rows × 10 columns

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diabetes_df_ml[list(diabetes_df_ml.columns)[10:20]]

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	number_outpatient	number_emergency	number_inpatient	diag_1	diag_2	diag_3	number_diagnoses	max_glu_serum	A1Cresult	metformin
0	0	0	0	1	7	7	1	2	2	1
1	0	0	0	6	1	6	9	2	2	1
2	2	0	1	8	1	8	6	2	2	1
3	0	0	0	6	1	0	7	2	2	1
4	0	0	0	6	6	1	5	2	2	1
...	...	...	...	...	...	...	...	...	...	...
71513	0	0	0	2	2	1	9	2	0	1
71514	0	1	0	3	3	9	9	2	1	1
71515	1	1	1	4	3	0	9	2	2	1
71516	0	1	0	6	6	6	9	2	2	1
71517	0	0	0	2	2	2	9	2	2	1

71518 rows × 10 columns

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diabetes_df_ml[list(diabetes_df_ml.columns)[20:30]]

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	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone
0	1	1	1	1	0	1	1	0	1	1
1	1	1	1	1	0	1	1	0	1	1
2	1	1	1	1	0	2	1	0	1	1
3	1	1	1	1	0	1	1	0	1	1
4	1	1	1	1	0	2	1	0	1	1
...	...	...	...	...	...	...	...	...	...	...
71513	1	1	1	1	0	1	3	0	1	1
71514	1	1	1	1	0	2	1	0	1	1
71515	1	1	1	1	0	1	1	0	1	1
71516	1	1	1	1	0	1	1	0	1	1
71517	1	1	1	1	0	1	1	0	1	1

71518 rows × 10 columns

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diabetes_df_ml[list(diabetes_df_ml.columns)[30:40]]

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	acarbose	miglitol	troglitazone	tolazamide	insulin	glyburide-metformin	glipizide-metformin	change	diabetesMed	readmitted
0	0	1	0	0	1	1	0	1	0	0
1	0	1	0	0	3	1	0	0	1	0
2	0	1	0	0	1	1	0	1	1	0
3	0	1	0	0	3	1	0	0	1	0
4	0	1	0	0	2	1	0	0	1	0
...	...	...	...	...	...	...	...	...	...	...
71513	0	1	0	0	2	1	0	0	1	0
71514	0	1	0	0	3	1	0	0	1	0
71515	0	1	0	0	2	1	0	1	1	0
71516	0	1	0	0	3	1	0	0	1	0
71517	0	1	0	0	1	1	0	1	0	0

71518 rows × 10 columns

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diabetes_df_ml[list(diabetes_df_ml.columns)[40:]]

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	Diag_Circulatory	Diag_Respiratory	Diag_Digestive	Diag_Diabetes	Diag_Injury	Diag_Musculoskeletal	Diag_Genitourinary	Diag_Neoplasms
0	0	0	0	1	0	0	0	0
1	0	0	0	1	0	0	0	1
2	0	0	0	1	0	0	0	0
3	1	0	0	1	0	0	0	1
4	0	0	0	1	0	0	0	1
...	...	...	...	...	...	...	...	...
71513	0	0	1	1	0	0	0	0
71514	0	1	0	0	0	0	1	0
71515	1	0	0	0	1	0	1	0
71516	0	0	0	0	0	0	0	1
71517	0	0	1	0	0	0	0	0

71518 rows × 8 columns

We can see that all the columns have numerical variables corresponding to their former category, so the method was successful

Shuffling rows

We shuffle the entire dataset in order to avoid an eventual order biais

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diabetes_df_ml = diabetes_df_ml.sample(n = len(diabetes_df_ml), random_state = 42)

Before start the use of the machine learning models, we can try to review the correlation matriw with the current changes of the columns and numerical value

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corr = diabetes_df_ml.corr()
corr.style.background_gradient(cmap='coolwarm').set_precision(2)

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color: #f1f1f1; }#T_0d7d1_row0_col11,#T_0d7d1_row3_col17,#T_0d7d1_row6_col0,#T_0d7d1_row14_col17,#T_0d7d1_row15_col10,#T_0d7d1_row18_col0,#T_0d7d1_row18_col39,#T_0d7d1_row20_col10,#T_0d7d1_row24_col10,#T_0d7d1_row26_col39,#T_0d7d1_row27_col10,#T_0d7d1_row28_col4,#T_0d7d1_row31_col10,#T_0d7d1_row32_col10,#T_0d7d1_row34_col4,#T_0d7d1_row34_col17,#T_0d7d1_row34_col39,#T_0d7d1_row35_col10,#T_0d7d1_row38_col33,#T_0d7d1_row40_col10,#T_0d7d1_row40_col11,#T_0d7d1_row42_col10,#T_0d7d1_row46_col0,#T_0d7d1_row46_col39,#T_0d7d1_row46_col41{ background-color: #4257c9; color: #f1f1f1; }#T_0d7d1_row0_col12,#T_0d7d1_row1_col4,#T_0d7d1_row1_col35,#T_0d7d1_row4_col39,#T_0d7d1_row5_col30,#T_0d7d1_row6_col1,#T_0d7d1_row6_col11,#T_0d7d1_row7_col21,#T_0d7d1_row7_col30,#T_0d7d1_row8_col30,#T_0d7d1_row10_col4,#T_0d7d1_row11_col30,#T_0d7d1_row11_col35,#T_0d7d1_row12_col0,#T_0d7d1_row14_col1,#T_0d7d1_row14_col10,#T_0d7d1_row15_col30,#T_0d7d1_row16_col17,#T_0d7d1_row17_col11,#T_0d7d1_row17_col35,#T_0d7d1_row18_col20,#T_0d7d1_row19_col0,#T_0d7d1_row20_col4,#T_0d7d1_row21_col30,#T_0d7d1_row23_col17,#T_0d7d1_row24_col30,#T_0d7d1_row27_col30,#T_0d7d1_row30_col0,#T_0d7d1_row31_col30,#T_0d7d1_row32_col30,#T_0d7d1_row33_col30,#T_0d7d1_row34_col12,#T_0d7d1_row34_col21,#T_0d7d1_row34_col30,#T_0d7d1_row34_col35,#T_0d7d1_row35_col21,#T_0d7d1_row36_col30,#T_0d7d1_row38_col0,#T_0d7d1_row39_col30,#T_0d7d1_row40_col12,#T_0d7d1_row41_col30,#T_0d7d1_row42_col44,#T_0d7d1_row43_col21,#T_0d7d1_row43_col35,#T_0d7d1_row44_col11,#T_0d7d1_row44_col12,#T_0d7d1_row44_col17,#T_0d7d1_row44_col21,#T_0d7d1_row46_col30{ background-color: #485fd1; color: #f1f1f1; }#T_0d7d1_row0_col13,#T_0d7d1_row4_col43,#T_0d7d1_row23_col19,#T_0d7d1_row29_col13,#T_0d7d1_row35_col13,#T_0d7d1_row40_col9,#T_0d7d1_row41_col40,#T_0d7d1_row45_col9,#T_0d7d1_row46_col2{ background-color: #94b6ff; color: #000000; }#T_0d7d1_row0_col14,#T_0d7d1_row4_col14,#T_0d7d1_row10_col14,#T_0d7d1_row17_col38,#T_0d7d1_row17_col40,#T_0d7d1_row18_col16,#T_0d7d1_row18_col40,#T_0d7d1_row20_col40,#T_0d7d1_row22_col40,#T_0d7d1_row34_col14,#T_0d7d1_row38_col16,#T_0d7d1_row39_col40{ background-color: #a2c1ff; color: #000000; }#T_0d7d1_row0_col15,#T_0d7d1_row3_col15,#T_0d7d1_row10_col15,#T_0d7d1_row10_col40,#T_0d7d1_row12_col15,#T_0d7d1_row13_col16,#T_0d7d1_row17_col16,#T_0d7d1_row18_col14,#T_0d7d1_row19_col13,#T_0d7d1_row19_col15,#T_0d7d1_row21_col14,#T_0d7d1_row21_col37,#T_0d7d1_row24_col16,#T_0d7d1_row25_col14,#T_0d7d1_row27_col15,#T_0d7d1_row28_col14,#T_0d7d1_row28_col43,#T_0d7d1_row29_col14,#T_0d7d1_row30_col15,#T_0d7d1_row30_col16,#T_0d7d1_row31_col40,#T_0d7d1_row31_col43,#T_0d7d1_row32_col14,#T_0d7d1_row32_col15,#T_0d7d1_row32_col16,#T_0d7d1_row32_col40,#T_0d7d1_row35_col14,#T_0d7d1_row35_col43,#T_0d7d1_row36_col14,#T_0d7d1_row36_col40,#T_0d7d1_row38_col15,#T_0d7d1_row39_col15,#T_0d7d1_row42_col14,#T_0d7d1_row46_col15{ background-color: #9ebeff; color: #000000; }#T_0d7d1_row0_col16,#T_0d7d1_row4_col38,#T_0d7d1_row8_col5,#T_0d7d1_row11_col14,#T_0d7d1_row11_col15,#T_0d7d1_row15_col13,#T_0d7d1_row20_col16,#T_0d7d1_row34_col15,#T_0d7d1_row37_col40,#T_0d7d1_row40_col8,#T_0d7d1_row47_col16{ background-color: #a3c2fe; color: #000000; }#T_0d7d1_row0_col17,#T_0d7d1_row2_col22,#T_0d7d1_row4_col0,#T_0d7d1_row4_col35,#T_0d7d1_row5_col10,#T_0d7d1_row7_col0,#T_0d7d1_row7_col3,#T_0d7d1_row7_col35,#T_0d7d1_row10_col39,#T_0d7d1_row11_col17,#T_0d7d1_row12_col17,#T_0d7d1_row12_col35,#T_0d7d1_row15_col17,#T_0d7d1_row17_col0,#T_0d7d1_row17_col12,#T_0d7d1_row18_col10,#T_0d7d1_row19_col10,#T_0d7d1_row20_col31,#T_0d7d1_row21_col0,#T_0d7d1_row21_col10,#T_0d7d1_row21_col31,#T_0d7d1_row23_col4,#T_0d7d1_row25_col17,#T_0d7d1_row25_col39,#T_0d7d1_row26_col10,#T_0d7d1_row28_col12,#T_0d7d1_row29_col10,#T_0d7d1_row29_col12,#T_0d7d1_row30_col12,#T_0d7d1_row30_col17,#T_0d7d1_row33_col12,#T_0d7d1_row36_col10,#T_0d7d1_row37_col0,#T_0d7d1_row37_col11,#T_0d7d1_row40_col0,#T_0d7d1_row40_col22,#T_0d7d1_row41_col45,#T_0d7d1_row41_col46,#T_0d7d1_row42_col17,#T_0d7d1_row46_col1,#T_0d7d1_row47_col45{ background-color: #4358cb; color: #f1f1f1; }#T_0d7d1_row0_col18,#T_0d7d1_row1_col0,#T_0d7d1_row1_col44,#T_0d7d1_row3_col46,#T_0d7d1_row4_col23,#T_0d7d1_row5_col0,#T_0d7d1_row6_col12,#T_0d7d1_row7_col4,#T_0d7d1_row10_col44,#T_0d7d1_row11_col44,#T_0d7d1_row18_col6,#T_0d7d1_row18_col23,#T_0d7d1_row23_col26,#T_0d7d1_row26_col1,#T_0d7d1_row29_col18,#T_0d7d1_row34_col3,#T_0d7d1_row38_col21,#T_0d7d1_row43_col7,#T_0d7d1_row47_col3{ background-color: #5875e1; color: #000000; }#T_0d7d1_row0_col19,#T_0d7d1_row5_col13,#T_0d7d1_row5_col19,#T_0d7d1_row7_col43,#T_0d7d1_row8_col15,#T_0d7d1_row17_col47,#T_0d7d1_row19_col26,#T_0d7d1_row20_col19,#T_0d7d1_row26_col2,#T_0d7d1_row29_col47,#T_0d7d1_row31_col19,#T_0d7d1_row34_col19,#T_0d7d1_row35_col47,#T_0d7d1_row37_col43,#T_0d7d1_row39_col13,#T_0d7d1_row39_col47,#T_0d7d1_row41_col19,#T_0d7d1_row44_col19,#T_0d7d1_row45_col16{ background-color: #8caffe; color: #000000; }#T_0d7d1_row0_col20,#T_0d7d1_row3_col1,#T_0d7d1_row6_col39,#T_0d7d1_row7_col1,#T_0d7d1_row7_col20,#T_0d7d1_row8_col4,#T_0d7d1_row9_col12,#T_0d7d1_row9_col21,#T_0d7d1_row11_col3,#T_0d7d1_row11_col20,#T_0d7d1_row12_col20,#T_0d7d1_row13_col11,#T_0d7d1_row19_col1,#T_0d7d1_row19_col20,#T_0d7d1_row25_col26,#T_0d7d1_row27_col3,#T_0d7d1_row33_col1,#T_0d7d1_row34_col1,#T_0d7d1_row34_col18,#T_0d7d1_row36_col1,#T_0d7d1_row36_col35,#T_0d7d1_row37_col4,#T_0d7d1_row38_col30,#T_0d7d1_row39_col3,#T_0d7d1_row40_col4,#T_0d7d1_row41_col3{ background-color: #506bda; color: #000000; }#T_0d7d1_row0_col22,#T_0d7d1_row0_col39,#T_0d7d1_row1_col22,#T_0d7d1_row1_col33,#T_0d7d1_row1_col39,#T_0d7d1_row2_col1,#T_0d7d1_row2_col27,#T_0d7d1_row2_col31,#T_0d7d1_row2_col33,#T_0d7d1_row3_col31,#T_0d7d1_row4_col17,#T_0d7d1_row4_col27,#T_0d7d1_row6_col22,#T_0d7d1_row6_col24,#T_0d7d1_row8_col39,#T_0d7d1_row9_col17,#T_0d7d1_row9_col22,#T_0d7d1_row14_col31,#T_0d7d1_row17_col39,#T_0d7d1_row18_col22,#T_0d7d1_row19_col31,#T_0d7d1_row21_col39,#T_0d7d1_row22_col39,#T_0d7d1_row23_col31,#T_0d7d1_row23_col39,#T_0d7d1_row25_col35,#T_0d7d1_row27_col39,#T_0d7d1_row29_col22,#T_0d7d1_row30_col39,#T_0d7d1_row31_col39,#T_0d7d1_row32_col39,#T_0d7d1_row33_col39,#T_0d7d1_row35_col39,#T_0d7d1_row36_col39,#T_0d7d1_row38_col4,#T_0d7d1_row38_col27,#T_0d7d1_row40_col27,#T_0d7d1_row40_col33,#T_0d7d1_row40_col45,#T_0d7d1_row41_col17,#T_0d7d1_row42_col41,#T_0d7d1_row43_col4,#T_0d7d1_row43_col22,#T_0d7d1_row43_col31,#T_0d7d1_row44_col10,#T_0d7d1_row44_col41,#T_0d7d1_row45_col31,#T_0d7d1_row45_col39,#T_0d7d1_row47_col0{ background-color: #3f53c6; 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color: #f1f1f1; }#T_0d7d1_row0_col25,#T_0d7d1_row2_col18,#T_0d7d1_row2_col41,#T_0d7d1_row3_col28,#T_0d7d1_row8_col42,#T_0d7d1_row10_col25,#T_0d7d1_row10_col29,#T_0d7d1_row11_col7,#T_0d7d1_row14_col28,#T_0d7d1_row14_col29,#T_0d7d1_row15_col6,#T_0d7d1_row17_col29,#T_0d7d1_row22_col28,#T_0d7d1_row22_col29,#T_0d7d1_row24_col29,#T_0d7d1_row27_col28,#T_0d7d1_row28_col5,#T_0d7d1_row29_col26,#T_0d7d1_row31_col29,#T_0d7d1_row39_col6,#T_0d7d1_row39_col29,#T_0d7d1_row41_col25,#T_0d7d1_row43_col5,#T_0d7d1_row44_col6,#T_0d7d1_row44_col29,#T_0d7d1_row46_col6,#T_0d7d1_row46_col28{ background-color: #6f92f3; color: #000000; }#T_0d7d1_row0_col26,#T_0d7d1_row1_col2,#T_0d7d1_row1_col7,#T_0d7d1_row3_col34,#T_0d7d1_row4_col29,#T_0d7d1_row5_col25,#T_0d7d1_row7_col28,#T_0d7d1_row7_col29,#T_0d7d1_row7_col41,#T_0d7d1_row9_col41,#T_0d7d1_row12_col25,#T_0d7d1_row12_col29,#T_0d7d1_row12_col42,#T_0d7d1_row14_col2,#T_0d7d1_row14_col44,#T_0d7d1_row15_col34,#T_0d7d1_row19_col8,#T_0d7d1_row21_col5,#T_0d7d1_row22_col42,#T_0d7d1_row24_col7,#T_0d7d1_row27_col5,#T_0d7d1_row27_col42,#T_0d7d1_row28_col26,#T_0d7d1_row31_col25,#T_0d7d1_row32_col7,#T_0d7d1_row33_col42,#T_0d7d1_row34_col5,#T_0d7d1_row39_col42,#T_0d7d1_row42_col9,#T_0d7d1_row44_col5{ background-color: #6c8ff1; 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color: #f1f1f1; }#T_0d7d1_row0_col34,#T_0d7d1_row4_col44,#T_0d7d1_row10_col46,#T_0d7d1_row12_col5,#T_0d7d1_row13_col18,#T_0d7d1_row13_col26,#T_0d7d1_row14_col45,#T_0d7d1_row15_col5,#T_0d7d1_row18_col44,#T_0d7d1_row25_col6,#T_0d7d1_row27_col34,#T_0d7d1_row28_col42,#T_0d7d1_row30_col34,#T_0d7d1_row31_col34,#T_0d7d1_row35_col25,#T_0d7d1_row42_col34,#T_0d7d1_row47_col2{ background-color: #6485ec; color: #000000; }#T_0d7d1_row0_col37,#T_0d7d1_row3_col37,#T_0d7d1_row8_col37,#T_0d7d1_row11_col16,#T_0d7d1_row31_col37,#T_0d7d1_row32_col37,#T_0d7d1_row44_col37{ background-color: #a9c6fd; color: #000000; }#T_0d7d1_row0_col38,#T_0d7d1_row16_col40,#T_0d7d1_row27_col38,#T_0d7d1_row31_col38,#T_0d7d1_row36_col38,#T_0d7d1_row38_col43{ background-color: #adc9fd; color: #000000; }#T_0d7d1_row0_col40,#T_0d7d1_row3_col43,#T_0d7d1_row9_col14,#T_0d7d1_row17_col14,#T_0d7d1_row17_col15,#T_0d7d1_row18_col15,#T_0d7d1_row20_col37,#T_0d7d1_row20_col43,#T_0d7d1_row21_col43,#T_0d7d1_row25_col16,#T_0d7d1_row25_col19,#T_0d7d1_row26_col14,#T_0d7d1_row26_col43,#T_0d7d1_row28_col15,#T_0d7d1_row29_col16,#T_0d7d1_row31_col16,#T_0d7d1_row36_col43,#T_0d7d1_row38_col26,#T_0d7d1_row39_col43,#T_0d7d1_row43_col37,#T_0d7d1_row46_col14{ background-color: #9bbcff; color: #000000; }#T_0d7d1_row0_col41,#T_0d7d1_row5_col23,#T_0d7d1_row6_col41,#T_0d7d1_row8_col1,#T_0d7d1_row11_col26,#T_0d7d1_row12_col26,#T_0d7d1_row13_col44,#T_0d7d1_row14_col26,#T_0d7d1_row15_col8,#T_0d7d1_row18_col26,#T_0d7d1_row20_col26,#T_0d7d1_row20_col34,#T_0d7d1_row21_col34,#T_0d7d1_row23_col6,#T_0d7d1_row24_col6,#T_0d7d1_row26_col42,#T_0d7d1_row28_col45,#T_0d7d1_row32_col34,#T_0d7d1_row33_col34,#T_0d7d1_row34_col26,#T_0d7d1_row35_col45,#T_0d7d1_row36_col34,#T_0d7d1_row39_col12,#T_0d7d1_row42_col18,#T_0d7d1_row44_col7,#T_0d7d1_row46_col26,#T_0d7d1_row47_col42{ background-color: #6384eb; color: #000000; }#T_0d7d1_row0_col42,#T_0d7d1_row6_col5,#T_0d7d1_row6_col26,#T_0d7d1_row6_col46,#T_0d7d1_row8_col25,#T_0d7d1_row10_col42,#T_0d7d1_row11_col2,#T_0d7d1_row11_col8,#T_0d7d1_row12_col28,#T_0d7d1_row14_col42,#T_0d7d1_row15_col28,#T_0d7d1_row15_col29,#T_0d7d1_row16_col29,#T_0d7d1_row16_col34,#T_0d7d1_row19_col5,#T_0d7d1_row20_col7,#T_0d7d1_row24_col42,#T_0d7d1_row26_col5,#T_0d7d1_row27_col29,#T_0d7d1_row30_col26,#T_0d7d1_row31_col42,#T_0d7d1_row32_col42,#T_0d7d1_row34_col25,#T_0d7d1_row34_col42,#T_0d7d1_row36_col29,#T_0d7d1_row36_col42,#T_0d7d1_row39_col25,#T_0d7d1_row41_col47,#T_0d7d1_row42_col40,#T_0d7d1_row45_col26,#T_0d7d1_row47_col6,#T_0d7d1_row47_col28,#T_0d7d1_row47_col29{ background-color: #6e90f2; color: #000000; }#T_0d7d1_row0_col43,#T_0d7d1_row3_col47,#T_0d7d1_row11_col47,#T_0d7d1_row15_col41,#T_0d7d1_row16_col13,#T_0d7d1_row16_col47,#T_0d7d1_row24_col13,#T_0d7d1_row34_col9,#T_0d7d1_row44_col9,#T_0d7d1_row45_col5,#T_0d7d1_row47_col43{ background-color: #93b5fe; color: #000000; }#T_0d7d1_row0_col46,#T_0d7d1_row1_col41,#T_0d7d1_row10_col12,#T_0d7d1_row10_col41,#T_0d7d1_row11_col5,#T_0d7d1_row11_col45,#T_0d7d1_row13_col46,#T_0d7d1_row14_col46,#T_0d7d1_row15_col23,#T_0d7d1_row16_col11,#T_0d7d1_row16_col12,#T_0d7d1_row18_col41,#T_0d7d1_row20_col45,#T_0d7d1_row22_col44,#T_0d7d1_row24_col44,#T_0d7d1_row26_col44,#T_0d7d1_row28_col18,#T_0d7d1_row29_col46,#T_0d7d1_row30_col44,#T_0d7d1_row31_col44,#T_0d7d1_row35_col18,#T_0d7d1_row36_col45,#T_0d7d1_row39_col45,#T_0d7d1_row43_col23,#T_0d7d1_row44_col42,#T_0d7d1_row45_col6,#T_0d7d1_row47_col23{ background-color: #5a78e4; color: #000000; }#T_0d7d1_row0_col47,#T_0d7d1_row2_col6,#T_0d7d1_row4_col19,#T_0d7d1_row7_col8,#T_0d7d1_row18_col47,#T_0d7d1_row20_col2,#T_0d7d1_row22_col19,#T_0d7d1_row23_col9,#T_0d7d1_row34_col37,#T_0d7d1_row37_col47,#T_0d7d1_row39_col19,#T_0d7d1_row43_col13{ background-color: #86a9fc; 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color: #000000; }#T_0d7d1_row1_col6,#T_0d7d1_row2_col20,#T_0d7d1_row3_col0,#T_0d7d1_row3_col18,#T_0d7d1_row9_col18,#T_0d7d1_row15_col18,#T_0d7d1_row16_col4,#T_0d7d1_row16_col10,#T_0d7d1_row18_col34,#T_0d7d1_row20_col44,#T_0d7d1_row21_col44,#T_0d7d1_row26_col46,#T_0d7d1_row30_col18,#T_0d7d1_row36_col18,#T_0d7d1_row37_col41,#T_0d7d1_row37_col44,#T_0d7d1_row38_col42,#T_0d7d1_row39_col41,#T_0d7d1_row41_col18,#T_0d7d1_row45_col42{ background-color: #5977e3; color: #000000; }#T_0d7d1_row1_col8,#T_0d7d1_row1_col19,#T_0d7d1_row1_col47,#T_0d7d1_row2_col13,#T_0d7d1_row9_col2,#T_0d7d1_row15_col19,#T_0d7d1_row20_col47,#T_0d7d1_row24_col47,#T_0d7d1_row25_col9,#T_0d7d1_row30_col47,#T_0d7d1_row31_col47,#T_0d7d1_row32_col47,#T_0d7d1_row33_col47,#T_0d7d1_row36_col47{ background-color: #8badfd; color: #000000; }#T_0d7d1_row1_col9,#T_0d7d1_row9_col45,#T_0d7d1_row18_col9,#T_0d7d1_row21_col8,#T_0d7d1_row26_col29,#T_0d7d1_row27_col8,#T_0d7d1_row35_col8,#T_0d7d1_row35_col28,#T_0d7d1_row36_col8,#T_0d7d1_row38_col7,#T_0d7d1_row39_col8{ background-color: #779af7; 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}#T_0d7d1_row1_col12,#T_0d7d1_row3_col35,#T_0d7d1_row5_col41,#T_0d7d1_row10_col17,#T_0d7d1_row11_col4,#T_0d7d1_row13_col17,#T_0d7d1_row17_col4,#T_0d7d1_row21_col12,#T_0d7d1_row23_col12,#T_0d7d1_row23_col35,#T_0d7d1_row24_col12,#T_0d7d1_row26_col17,#T_0d7d1_row26_col21,#T_0d7d1_row27_col17,#T_0d7d1_row31_col12,#T_0d7d1_row32_col12,#T_0d7d1_row33_col0,#T_0d7d1_row36_col12,#T_0d7d1_row37_col12,#T_0d7d1_row38_col22,#T_0d7d1_row39_col17,#T_0d7d1_row42_col12,#T_0d7d1_row42_col30,#T_0d7d1_row42_col35,#T_0d7d1_row45_col10,#T_0d7d1_row45_col41,#T_0d7d1_row46_col17,#T_0d7d1_row46_col35,#T_0d7d1_row47_col10{ background-color: #445acc; color: #f1f1f1; }#T_0d7d1_row1_col13,#T_0d7d1_row4_col8,#T_0d7d1_row8_col19,#T_0d7d1_row9_col34,#T_0d7d1_row19_col25,#T_0d7d1_row23_col28,#T_0d7d1_row27_col2,#T_0d7d1_row30_col2,#T_0d7d1_row41_col43,#T_0d7d1_row46_col40{ background-color: #7ea1fa; color: #000000; }#T_0d7d1_row1_col15,#T_0d7d1_row3_col16,#T_0d7d1_row4_col2,#T_0d7d1_row7_col37,#T_0d7d1_row8_col6,#T_0d7d1_row16_col7,#T_0d7d1_row18_col13,#T_0d7d1_row18_col38,#T_0d7d1_row22_col15,#T_0d7d1_row22_col16,#T_0d7d1_row23_col15,#T_0d7d1_row23_col43,#T_0d7d1_row25_col15,#T_0d7d1_row33_col16,#T_0d7d1_row37_col14,#T_0d7d1_row42_col38{ background-color: #9abbff; 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color: #000000; }#T_0d7d1_row1_col20,#T_0d7d1_row3_col45,#T_0d7d1_row4_col20,#T_0d7d1_row5_col1,#T_0d7d1_row9_col30,#T_0d7d1_row10_col20,#T_0d7d1_row13_col3,#T_0d7d1_row13_col20,#T_0d7d1_row15_col20,#T_0d7d1_row16_col3,#T_0d7d1_row16_col39,#T_0d7d1_row17_col3,#T_0d7d1_row18_col3,#T_0d7d1_row18_col11,#T_0d7d1_row19_col46,#T_0d7d1_row20_col11,#T_0d7d1_row22_col20,#T_0d7d1_row23_col11,#T_0d7d1_row25_col23,#T_0d7d1_row27_col4,#T_0d7d1_row28_col11,#T_0d7d1_row29_col11,#T_0d7d1_row30_col4,#T_0d7d1_row34_col20,#T_0d7d1_row35_col11,#T_0d7d1_row35_col20,#T_0d7d1_row37_col3,#T_0d7d1_row38_col12,#T_0d7d1_row40_col21,#T_0d7d1_row41_col0,#T_0d7d1_row41_col1,#T_0d7d1_row44_col20,#T_0d7d1_row45_col0,#T_0d7d1_row45_col20,#T_0d7d1_row46_col21,#T_0d7d1_row47_col1,#T_0d7d1_row47_col20{ background-color: #4c66d6; color: #000000; }#T_0d7d1_row1_col21,#T_0d7d1_row3_col21,#T_0d7d1_row7_col39,#T_0d7d1_row8_col21,#T_0d7d1_row11_col1,#T_0d7d1_row11_col39,#T_0d7d1_row12_col21,#T_0d7d1_row13_col0,#T_0d7d1_row13_col40,#T_0d7d1_row14_col21,#T_0d7d1_row15_col21,#T_0d7d1_row15_col35,#T_0d7d1_row16_col30,#T_0d7d1_row16_col45,#T_0d7d1_row18_col1,#T_0d7d1_row20_col0,#T_0d7d1_row22_col21,#T_0d7d1_row23_col0,#T_0d7d1_row25_col0,#T_0d7d1_row25_col20,#T_0d7d1_row27_col21,#T_0d7d1_row33_col21,#T_0d7d1_row35_col0,#T_0d7d1_row36_col21,#T_0d7d1_row38_col10,#T_0d7d1_row38_col39,#T_0d7d1_row39_col21,#T_0d7d1_row41_col21,#T_0d7d1_row41_col35,#T_0d7d1_row42_col20,#T_0d7d1_row45_col11,#T_0d7d1_row45_col17,#T_0d7d1_row45_col30,#T_0d7d1_row46_col3,#T_0d7d1_row46_col10,#T_0d7d1_row46_col45{ background-color: #4961d2; 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color: #000000; }#T_0d7d1_row1_col40,#T_0d7d1_row4_col16,#T_0d7d1_row6_col15,#T_0d7d1_row8_col38,#T_0d7d1_row12_col37,#T_0d7d1_row13_col37,#T_0d7d1_row13_col38,#T_0d7d1_row19_col43,#T_0d7d1_row24_col37,#T_0d7d1_row26_col19,#T_0d7d1_row36_col37,#T_0d7d1_row40_col38,#T_0d7d1_row44_col15,#T_0d7d1_row46_col38{ background-color: #aac7fd; color: #000000; }#T_0d7d1_row1_col45,#T_0d7d1_row5_col46,#T_0d7d1_row8_col3,#T_0d7d1_row28_col1,#T_0d7d1_row28_col20,#T_0d7d1_row28_col21,#T_0d7d1_row31_col20,#T_0d7d1_row39_col11,#T_0d7d1_row39_col20,#T_0d7d1_row40_col20,#T_0d7d1_row41_col5,#T_0d7d1_row44_col4,#T_0d7d1_row46_col44,#T_0d7d1_row47_col11{ background-color: #536edd; color: #000000; }#T_0d7d1_row2_col9,#T_0d7d1_row6_col19,#T_0d7d1_row10_col19,#T_0d7d1_row16_col46,#T_0d7d1_row17_col19,#T_0d7d1_row18_col43,#T_0d7d1_row22_col47,#T_0d7d1_row24_col19,#T_0d7d1_row25_col47,#T_0d7d1_row27_col47,#T_0d7d1_row28_col47,#T_0d7d1_row29_col9,#T_0d7d1_row32_col19,#T_0d7d1_row33_col19,#T_0d7d1_row36_col19,#T_0d7d1_row41_col9,#T_0d7d1_row46_col7{ background-color: #89acfd; color: #000000; }#T_0d7d1_row2_col14,#T_0d7d1_row13_col43,#T_0d7d1_row16_col8,#T_0d7d1_row19_col47,#T_0d7d1_row21_col13,#T_0d7d1_row28_col13,#T_0d7d1_row37_col16{ background-color: #92b4fe; color: #000000; }#T_0d7d1_row2_col15,#T_0d7d1_row3_col19,#T_0d7d1_row7_col13,#T_0d7d1_row8_col43,#T_0d7d1_row12_col9,#T_0d7d1_row26_col9,#T_0d7d1_row26_col47,#T_0d7d1_row37_col2,#T_0d7d1_row37_col42,#T_0d7d1_row44_col2,#T_0d7d1_row45_col40{ background-color: #85a8fc; color: #000000; }#T_0d7d1_row2_col16{ background-color: #d6dce4; color: #000000; }#T_0d7d1_row2_col19,#T_0d7d1_row2_col25,#T_0d7d1_row2_col46,#T_0d7d1_row4_col7,#T_0d7d1_row9_col43,#T_0d7d1_row12_col11,#T_0d7d1_row16_col19,#T_0d7d1_row16_col41,#T_0d7d1_row20_col8,#T_0d7d1_row21_col28,#T_0d7d1_row22_col8,#T_0d7d1_row23_col29,#T_0d7d1_row30_col8,#T_0d7d1_row31_col8,#T_0d7d1_row33_col8,#T_0d7d1_row34_col7,#T_0d7d1_row40_col43,#T_0d7d1_row42_col8,#T_0d7d1_row44_col47,#T_0d7d1_row45_col28,#T_0d7d1_row46_col19{ background-color: #799cf8; color: #000000; 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	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	diag_1	diag_2	diag_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	insulin	glyburide-metformin	glipizide-metformin	change	diabetesMed	readmitted	Diag_Circulatory	Diag_Respiratory	Diag_Digestive	Diag_Diabetes	Diag_Injury	Diag_Musculoskeletal	Diag_Genitourinary	Diag_Neoplasms
race	1.00	0.06	0.10	0.06	-0.01	0.06	-0.01	-0.01	0.03	0.03	0.03	-0.03	0.01	0.01	0.01	0.00	0.03	-0.01	-0.02	0.01	0.01	-0.01	0.00	0.01	0.00	0.01	0.02	0.00	0.01	0.00	0.01	-0.00	0.00	-0.00	0.00	0.01	0.00	-0.01	0.01	-0.00	-0.01	0.02	0.00	-0.04	0.02	0.02	-0.01	-0.02
gender	0.06	1.00	-0.05	0.00	-0.01	-0.01	-0.02	0.00	0.07	-0.01	-0.01	-0.02	-0.00	-0.07	-0.02	-0.02	-0.00	-0.00	-0.02	0.00	-0.00	-0.00	0.00	-0.00	-0.00	0.03	0.03	0.00	0.01	0.01	0.01	0.00	0.01	0.00	0.00	0.00	0.00	-0.02	0.02	-0.00	0.05	-0.01	-0.02	0.00	-0.01	-0.03	-0.04	-0.01
age	0.10	-0.05	1.00	0.01	0.11	-0.01	0.13	0.03	-0.03	0.05	0.03	-0.05	0.02	-0.02	-0.05	-0.10	0.25	0.00	0.07	-0.06	0.04	0.01	0.01	0.03	0.00	0.04	0.06	0.01	0.01	0.00	0.01	0.01	-0.00	0.01	-0.02	-0.00	0.00	0.04	-0.03	0.04	0.21	0.06	-0.03	-0.25	0.03	-0.00	0.09	-0.08
admission_type_id	0.06	0.00	0.01	1.00	-0.04	0.18	0.02	-0.05	0.01	-0.08	0.02	0.00	0.00	-0.01	0.01	0.00	-0.01	-0.01	-0.01	-0.02	0.02	-0.00	-0.01	0.01	-0.00	-0.00	0.00	0.00	-0.00	0.02	-0.01	0.01	-0.00	-0.00	0.03	-0.01	-0.01	-0.01	0.02	0.00	0.01	0.00	0.01	-0.01	-0.02	-0.05	-0.02	0.03
discharge_disposition_id	-0.01	-0.01	0.11	-0.04	1.00	0.00	0.16	0.05	0.02	0.10	-0.01	-0.02	0.00	-0.01	0.01	0.02	0.05	-0.02	0.00	-0.01	-0.01	-0.01	0.02	-0.02	0.00	-0.01	0.04	0.01	-0.03	-0.01	0.01	0.00	0.01	0.00	-0.03	-0.01	0.01	0.02	-0.04	0.03	0.02	0.00	-0.05	-0.04	0.03	0.04	0.00	0.01
admission_source_id	0.06	-0.01	-0.01	0.18	0.00	1.00	0.01	-0.18	0.19	0.10	0.00	-0.06	-0.02	-0.02	-0.03	-0.02	-0.14	0.02	0.02	0.01	-0.01	0.00	-0.00	0.02	-0.00	0.00	0.01	0.00	0.02	0.02	-0.00	-0.00	-0.01	-0.00	0.01	-0.00	-0.00	0.02	0.00	-0.01	0.03	-0.09	-0.05	0.01	0.01	0.13	-0.04	-0.06
time_in_hospital	-0.01	-0.02	0.13	0.02	0.16	0.01	1.00	0.33	0.19	0.47	-0.01	-0.01	0.06	0.02	0.07	0.05	0.23	-0.03	-0.03	-0.00	0.03	0.01	0.00	0.01	0.01	0.02	0.02	0.00	0.01	0.01	0.01	-0.00	0.00	-0.01	0.06	-0.00	0.00	-0.11	0.07	0.05	-0.03	0.02	0.01	-0.14	0.05	-0.02	0.05	0.05
num_lab_procedures	-0.01	0.00	0.03	-0.05	0.05	-0.18	0.33	1.00	0.05	0.26	-0.01	0.01	0.08	-0.04	0.03	0.03	0.16	0.00	-0.12	-0.04	0.01	-0.01	-0.00	-0.00	0.00	0.02	-0.00	-0.00	-0.01	-0.01	0.00	-0.00	0.00	-0.00	0.05	-0.01	-0.00	-0.07	0.04	0.03	-0.01	0.05	0.03	-0.07	-0.03	-0.12	0.10	0.04
num_procedures	0.03	0.07	-0.03	0.01	0.02	0.19	0.19	0.05	1.00	0.40	-0.02	-0.04	-0.02	-0.24	-0.12	-0.07	0.09	0.02	0.01	-0.04	0.00	-0.00	0.00	0.01	0.01	0.01	0.01	-0.00	0.01	0.01	0.00	0.00	-0.01	0.00	0.01	-0.00	-0.00	-0.01	-0.01	-0.00	0.15	-0.12	0.00	-0.10	0.08	0.02	-0.05	-0.16
num_medications	0.03	-0.01	0.05	-0.08	0.10	0.10	0.47	0.26	0.40	1.00	0.03	0.00	0.04	-0.05	-0.01	-0.02	0.26	-0.02	-0.01	0.07	0.02	0.02	0.00	0.04	0.01	0.06	0.04	0.00	0.07	0.05	0.02	0.00	0.00	-0.00	0.09	0.01	0.00	-0.25	0.19	0.03	0.09	0.05	-0.05	-0.14	0.09	0.09	0.01	-0.11
number_outpatient	0.03	-0.01	0.03	0.02	-0.01	0.00	-0.01	-0.01	-0.02	0.03	1.00	0.10	0.07	0.02	0.02	0.00	0.08	-0.01	0.00	0.00	-0.00	0.00	-0.01	-0.00	-0.00	0.01	0.00	-0.00	0.02	0.00	0.01	0.00	-0.00	-0.00	0.02	-0.00	0.00	-0.02	0.02	0.01	-0.00	-0.01	-0.00	-0.03	-0.01	0.01	0.02	0.01
number_emergency	-0.03	-0.02	-0.05	0.00	-0.02	-0.06	-0.01	0.01	-0.04	0.00	0.10	1.00	0.15	0.02	0.02	0.02	0.05	-0.01	0.01	-0.00	0.01	0.00	-0.00	0.01	-0.00	-0.00	-0.02	-0.00	0.01	0.00	0.00	0.00	-0.00	-0.00	0.01	0.01	-0.00	-0.03	0.03	0.03	-0.03	0.01	0.01	0.01	-0.01	-0.00	0.01	0.03
number_inpatient	0.01	-0.00	0.02	0.00	0.00	-0.02	0.06	0.08	-0.02	0.04	0.07	0.15	1.00	0.02	0.00	0.00	0.07	-0.01	0.02	-0.04	0.01	-0.00	-0.00	-0.00	-0.00	0.00	-0.01	-0.00	-0.01	-0.01	-0.01	-0.00	-0.00	-0.00	0.01	-0.01	-0.00	-0.00	0.02	0.10	0.01	0.00	-0.00	-0.03	0.01	-0.02	0.01	0.00
diag_1	0.01	-0.07	-0.02	-0.01	-0.01	-0.02	0.02	-0.04	-0.24	-0.05	0.02	0.02	0.02	1.00	0.14	0.07	0.01	-0.00	0.03	0.05	-0.01	0.00	-0.01	-0.01	0.01	-0.01	-0.01	-0.00	0.00	0.01	-0.00	-0.01	-0.00	-0.00	-0.01	0.01	-0.00	-0.00	-0.01	-0.02	-0.36	0.43	-0.09	-0.04	0.03	0.10	-0.01	0.18
diag_2	0.01	-0.02	-0.05	0.01	0.01	-0.03	0.07	0.03	-0.12	-0.01	0.02	0.02	0.00	0.14	1.00	0.08	0.08	-0.01	-0.00	0.03	-0.01	-0.00	-0.01	-0.01	0.01	-0.00	-0.01	-0.01	-0.00	-0.00	-0.01	0.01	0.00	-0.01	0.01	-0.00	-0.00	-0.02	0.01	-0.01	-0.43	0.28	-0.00	-0.13	0.06	0.03	-0.01	0.24
diag_3	0.00	-0.02	-0.10	0.00	0.02	-0.02	0.05	0.03	-0.07	-0.02	0.00	0.02	0.00	0.07	0.08	1.00	0.04	-0.01	-0.01	0.00	-0.01	-0.00	-0.02	-0.01	-0.00	-0.01	-0.02	0.00	-0.01	-0.00	-0.00	-0.00	-0.00	-0.00	0.02	0.01	0.01	-0.02	0.00	0.00	-0.42	0.18	-0.02	-0.16	0.05	0.01	0.00	0.26
number_diagnoses	0.03	-0.00	0.25	-0.01	0.05	-0.14	0.23	0.16	0.09	0.26	0.08	0.05	0.07	0.01	0.08	0.04	1.00	0.01	0.02	-0.06	0.03	0.02	-0.01	0.01	0.00	-0.01	-0.02	0.00	0.01	-0.01	0.01	-0.00	0.00	-0.01	0.03	-0.00	0.00	-0.05	0.02	0.04	0.06	0.09	0.01	-0.42	0.07	-0.07	0.14	0.02
max_glu_serum	-0.01	-0.00	0.00	-0.01	-0.02	0.02	-0.03	0.00	0.02	-0.02	-0.01	-0.01	-0.01	-0.00	-0.01	-0.01	0.01	1.00	0.00	-0.00	0.00	0.00	-0.00	0.01	0.00	-0.01	-0.01	-0.01	0.00	0.00	-0.01	0.00	0.00	-0.00	-0.01	0.00	0.00	0.04	-0.04	-0.00	0.02	-0.02	-0.01	-0.02	0.01	0.01	-0.00	-0.00
A1Cresult	-0.02	-0.02	0.07	-0.01	0.00	0.02	-0.03	-0.12	0.01	-0.01	0.00	0.01	0.02	0.03	-0.00	-0.01	0.02	0.00	1.00	-0.05	-0.02	0.00	0.00	-0.02	0.00	-0.02	-0.02	0.00	-0.00	-0.01	-0.01	-0.00	0.00	-0.00	-0.04	-0.00	-0.01	0.08	-0.07	0.00	0.01	-0.01	0.03	-0.08	0.03	0.01	0.00	-0.02
metformin	0.01	0.00	-0.06	-0.02	-0.01	0.01	-0.00	-0.04	-0.04	0.07	0.00	-0.00	-0.04	0.05	0.03	0.00	-0.06	-0.00	-0.05	1.00	0.01	0.02	-0.01	0.04	-0.00	0.06	0.12	-0.01	0.06	0.08	0.01	0.01	-0.00	0.00	0.01	-0.02	-0.00	-0.32	0.26	-0.01	-0.03	0.01	-0.05	0.06	0.01	0.07	-0.06	0.02
repaglinide	0.01	-0.00	0.04	0.02	-0.01	-0.01	0.03	0.01	0.00	0.02	-0.00	0.01	0.01	-0.01	-0.01	-0.01	0.03	0.00	-0.02	0.01	1.00	0.00	-0.00	-0.01	-0.00	-0.02	-0.01	-0.00	0.02	0.02	0.01	0.02	-0.00	-0.00	-0.00	-0.00	-0.00	-0.07	0.06	0.02	0.02	0.00	-0.02	-0.01	-0.01	-0.00	0.01	-0.00
nateglinide	-0.01	-0.00	0.01	-0.00	-0.01	0.00	0.01	-0.01	-0.00	0.02	0.00	0.00	-0.00	0.00	-0.00	-0.00	0.02	0.00	0.00	0.02	0.00	1.00	-0.00	0.01	-0.00	-0.01	-0.02	-0.00	0.03	0.01	0.00	0.02	-0.00	-0.00	-0.01	-0.01	-0.00	-0.05	0.04	-0.00	0.01	-0.00	-0.01	-0.01	-0.01	0.00	0.01	0.01
chlorpropamide	0.00	0.00	0.01	-0.01	0.02	-0.00	0.00	-0.00	0.00	0.00	-0.01	-0.00	-0.00	-0.01	-0.01	-0.02	-0.01	-0.00	0.00	-0.01	-0.00	-0.00	1.00	-0.01	-0.00	-0.01	-0.01	-0.00	-0.01	0.00	-0.00	-0.00	-0.00	-0.00	-0.01	-0.00	-0.00	0.01	0.02	-0.01	0.01	-0.00	-0.01	0.00	-0.00	0.01	-0.00	-0.01
glimepiride	0.01	-0.00	0.03	0.01	-0.02	0.02	0.01	-0.00	0.01	0.04	-0.00	0.01	-0.00	-0.01	-0.01	-0.01	0.01	0.01	-0.02	0.04	-0.01	0.01	-0.01	1.00	-0.00	-0.06	-0.06	-0.00	0.04	0.03	0.01	0.01	0.01	-0.00	0.01	-0.01	-0.00	-0.13	0.12	-0.00	0.03	0.01	-0.02	-0.01	0.00	0.00	-0.01	-0.01
acetohexamide	0.00	-0.00	0.00	-0.00	0.00	-0.00	0.01	0.00	0.01	0.01	-0.00	-0.00	-0.00	0.01	0.01	-0.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	1.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	0.00	-0.00	0.00	0.01	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
glipizide	0.01	0.03	0.04	-0.00	-0.01	0.00	0.02	0.02	0.01	0.06	0.01	-0.00	0.00	-0.01	-0.00	-0.01	-0.01	-0.01	-0.02	0.06	-0.02	-0.01	-0.01	-0.06	-0.00	1.00	-0.08	-0.01	0.04	0.04	0.02	0.00	-0.00	-0.01	0.01	-0.03	-0.00	-0.18	0.19	0.01	0.02	0.01	-0.04	-0.00	-0.00	0.02	-0.01	-0.01
glyburide	0.02	0.03	0.06	0.00	0.04	0.01	0.02	-0.00	0.01	0.04	0.00	-0.02	-0.01	-0.01	-0.01	-0.02	-0.02	-0.01	-0.02	0.12	-0.01	-0.02	-0.01	-0.06	-0.00	-0.08	1.00	-0.00	0.02	0.03	0.02	-0.00	-0.00	-0.01	-0.02	-0.01	0.00	-0.17	0.17	0.01	0.04	0.01	-0.04	-0.01	-0.00	0.02	-0.02	-0.03
tolbutamide	0.00	0.00	0.01	0.00	0.01	0.00	0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.01	0.00	0.00	-0.01	0.00	-0.01	-0.00	-0.00	-0.00	-0.00	-0.00	-0.01	-0.00	1.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	0.01	-0.00	0.01	0.00	-0.00	-0.00	0.01	0.00	0.00	-0.01
pioglitazone	0.01	0.01	0.01	-0.00	-0.03	0.02	0.01	-0.01	0.01	0.07	0.02	0.01	-0.01	0.00	-0.00	-0.01	0.01	0.00	-0.00	0.06	0.02	0.03	-0.01	0.04	-0.00	0.04	0.02	-0.00	1.00	-0.06	0.01	0.00	-0.00	-0.00	0.00	0.02	0.00	-0.21	0.15	-0.00	0.01	-0.00	-0.03	0.00	0.02	0.03	-0.00	-0.01
rosiglitazone	0.00	0.01	0.00	0.02	-0.01	0.02	0.01	-0.01	0.01	0.05	0.00	0.00	-0.01	0.01	-0.00	-0.00	-0.01	0.00	-0.01	0.08	0.02	0.01	0.00	0.03	-0.00	0.04	0.03	-0.00	-0.06	1.00	0.01	0.00	0.01	0.00	0.01	0.01	-0.00	-0.20	0.14	0.00	0.01	0.01	-0.03	0.01	-0.00	0.02	-0.01	-0.00
acarbose	0.01	0.01	0.01	-0.01	0.01	-0.00	0.01	0.00	0.00	0.02	0.01	0.00	-0.01	-0.00	-0.01	-0.00	0.01	-0.01	-0.01	0.01	0.01	0.00	-0.00	0.01	-0.00	0.02	0.02	-0.00	0.01	0.01	1.00	-0.00	-0.00	-0.00	0.00	0.01	-0.00	-0.05	0.03	-0.00	0.01	-0.00	-0.01	-0.00	-0.00	0.00	0.00	-0.00
miglitol	-0.00	0.00	0.01	0.01	0.00	-0.00	-0.00	-0.00	0.00	0.00	0.00	0.00	-0.00	-0.01	0.01	-0.00	-0.00	0.00	-0.00	0.01	0.02	0.02	-0.00	0.01	-0.00	0.00	-0.00	-0.00	0.00	0.00	-0.00	1.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.01	0.01	-0.00	-0.00	0.00	0.00	0.01	-0.00	0.00	-0.00	-0.01
troglitazone	0.00	0.01	-0.00	-0.00	0.01	-0.01	0.00	0.00	-0.01	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.01	-0.00	-0.00	-0.00	-0.00	-0.00	0.01	-0.00	-0.00	1.00	-0.00	-0.00	-0.00	-0.00	-0.01	0.00	-0.00	0.00	0.00	-0.00	-0.00	0.01	0.01	-0.00	-0.01
tolazamide	-0.00	0.00	0.01	-0.00	0.00	-0.00	-0.01	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.01	-0.00	-0.01	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.01	-0.01	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	1.00	-0.00	-0.00	-0.00	0.00	0.01	-0.00	0.00	0.00	-0.00	-0.00	0.00	0.01	-0.01	-0.01
insulin	0.00	0.00	-0.02	0.03	-0.03	0.01	0.06	0.05	0.01	0.09	0.02	0.01	0.01	-0.01	0.01	0.02	0.03	-0.01	-0.04	0.01	-0.00	-0.01	-0.01	0.01	0.00	0.01	-0.02	-0.00	0.00	0.01	0.00	0.00	-0.00	-0.00	1.00	0.00	-0.00	-0.16	0.28	0.00	-0.02	0.01	0.00	0.01	0.00	-0.02	0.01	0.01
glyburide-metformin	0.01	0.00	-0.00	-0.01	-0.01	-0.00	-0.00	-0.01	-0.00	0.01	-0.00	0.01	-0.01	0.01	-0.00	0.01	-0.00	0.00	-0.00	-0.02	-0.00	-0.01	-0.00	-0.01	-0.00	-0.03	-0.01	-0.00	0.02	0.01	0.01	-0.00	-0.00	-0.00	0.00	1.00	0.03	-0.04	0.05	-0.00	-0.01	0.01	-0.01	0.00	0.01	0.02	-0.01	-0.00
glipizide-metformin	0.00	0.00	0.00	-0.01	0.01	-0.00	0.00	-0.00	-0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.01	0.00	0.00	-0.01	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.03	1.00	-0.01	0.01	-0.00	-0.00	-0.00	0.00	-0.00	0.01	-0.00	-0.00	-0.01
change	-0.01	-0.02	0.04	-0.01	0.02	0.02	-0.11	-0.07	-0.01	-0.25	-0.02	-0.03	-0.00	-0.00	-0.02	-0.02	-0.05	0.04	0.08	-0.32	-0.07	-0.05	0.01	-0.13	-0.00	-0.18	-0.17	0.00	-0.21	-0.20	-0.05	-0.01	-0.01	0.00	-0.16	-0.04	-0.01	1.00	-0.51	-0.02	0.02	-0.01	0.08	-0.07	-0.01	-0.02	0.01	-0.02
diabetesMed	0.01	0.02	-0.03	0.02	-0.04	0.00	0.07	0.04	-0.01	0.19	0.02	0.03	0.02	-0.01	0.01	0.00	0.02	-0.04	-0.07	0.26	0.06	0.04	0.02	0.12	0.00	0.19	0.17	0.01	0.15	0.14	0.03	0.01	0.00	0.01	0.28	0.05	0.01	-0.51	1.00	0.03	-0.01	0.01	-0.08	0.07	0.00	0.02	-0.00	0.00
readmitted	-0.00	-0.00	0.04	0.00	0.03	-0.01	0.05	0.03	-0.00	0.03	0.01	0.03	0.10	-0.02	-0.01	0.00	0.04	-0.00	0.00	-0.01	0.02	-0.00	-0.01	-0.00	-0.00	0.01	0.01	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.02	0.03	1.00	0.02	-0.01	-0.01	-0.01	0.02	-0.01	0.01	0.00
Diag_Circulatory	-0.01	0.05	0.21	0.01	0.02	0.03	-0.03	-0.01	0.15	0.09	-0.00	-0.03	0.01	-0.36	-0.43	-0.42	0.06	0.02	0.01	-0.03	0.02	0.01	0.01	0.03	0.00	0.02	0.04	0.01	0.01	0.01	0.01	-0.00	0.00	0.00	-0.02	-0.01	-0.00	0.02	-0.01	0.02	1.00	-0.04	-0.19	-0.14	-0.12	-0.10	-0.13	-0.33
Diag_Respiratory	0.02	-0.01	0.06	0.00	0.00	-0.09	0.02	0.05	-0.12	0.05	-0.01	0.01	0.00	0.43	0.28	0.18	0.09	-0.02	-0.01	0.01	0.00	-0.00	-0.00	0.01	0.01	0.01	0.01	0.00	-0.00	0.01	-0.00	0.00	0.00	0.00	0.01	0.01	-0.00	-0.01	0.01	-0.01	-0.04	1.00	-0.10	-0.12	-0.10	-0.09	-0.10	-0.12
Diag_Digestive	0.00	-0.02	-0.03	0.01	-0.05	-0.05	0.01	0.03	0.00	-0.05	-0.00	0.01	-0.00	-0.09	-0.00	-0.02	0.01	-0.01	0.03	-0.05	-0.02	-0.01	-0.01	-0.02	-0.00	-0.04	-0.04	-0.00	-0.03	-0.03	-0.01	0.00	-0.00	-0.00	0.00	-0.01	0.00	0.08	-0.08	-0.01	-0.19	-0.10	1.00	-0.05	-0.07	-0.08	-0.05	-0.04
Diag_Diabetes	-0.04	0.00	-0.25	-0.01	-0.04	0.01	-0.14	-0.07	-0.10	-0.14	-0.03	0.01	-0.03	-0.04	-0.13	-0.16	-0.42	-0.02	-0.08	0.06	-0.01	-0.01	0.00	-0.01	-0.00	-0.00	-0.01	-0.00	0.00	0.01	-0.00	0.01	-0.00	-0.00	0.01	0.00	-0.00	-0.07	0.07	-0.01	-0.14	-0.12	-0.05	1.00	-0.11	0.04	-0.13	-0.04
Diag_Injury	0.02	-0.01	0.03	-0.02	0.03	0.01	0.05	-0.03	0.08	0.09	-0.01	-0.01	0.01	0.03	0.06	0.05	0.07	0.01	0.03	0.01	-0.01	-0.01	-0.00	0.00	-0.00	-0.00	-0.00	0.01	0.02	-0.00	-0.00	-0.00	0.01	0.00	0.00	0.01	0.01	-0.01	0.00	0.02	-0.12	-0.10	-0.07	-0.11	1.00	-0.02	-0.03	-0.07
Diag_Musculoskeletal	0.02	-0.03	-0.00	-0.05	0.04	0.13	-0.02	-0.12	0.02	0.09	0.01	-0.00	-0.02	0.10	0.03	0.01	-0.07	0.01	0.01	0.07	-0.00	0.00	0.01	0.00	-0.00	0.02	0.02	0.00	0.03	0.02	0.00	0.00	0.01	0.01	-0.02	0.02	-0.00	-0.02	0.02	-0.01	-0.10	-0.09	-0.08	0.04	-0.02	1.00	-0.06	-0.09
Diag_Genitourinary	-0.01	-0.04	0.09	-0.02	0.00	-0.04	0.05	0.10	-0.05	0.01	0.02	0.01	0.01	-0.01	-0.01	0.00	0.14	-0.00	0.00	-0.06	0.01	0.01	-0.00	-0.01	-0.00	-0.01	-0.02	0.00	-0.00	-0.01	0.00	-0.00	-0.00	-0.01	0.01	-0.01	-0.00	0.01	-0.00	0.01	-0.13	-0.10	-0.05	-0.13	-0.03	-0.06	1.00	0.01
Diag_Neoplasms	-0.02	-0.01	-0.08	0.03	0.01	-0.06	0.05	0.04	-0.16	-0.11	0.01	0.03	0.00	0.18	0.24	0.26	0.02	-0.00	-0.02	0.02	-0.00	0.01	-0.01	-0.01	-0.00	-0.01	-0.03	-0.01	-0.01	-0.00	-0.00	-0.01	-0.01	-0.01	0.01	-0.00	-0.01	-0.02	0.00	0.00	-0.33	-0.12	-0.04	-0.04	-0.07	-0.09	0.01	1.00

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	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	diag_1	diag_2	diag_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	insulin	glyburide-metformin	glipizide-metformin	change	diabetesMed	readmitted	Diag_Circulatory	Diag_Respiratory	Diag_Digestive	Diag_Diabetes	Diag_Injury	Diag_Musculoskeletal	Diag_Genitourinary	Diag_Neoplasms
race	1.00	0.06	0.10	0.06	-0.01	0.06	-0.01	-0.01	0.03	0.03	0.03	-0.03	0.01	0.01	0.01	0.00	0.03	-0.01	-0.02	0.01	0.01	-0.01	0.00	0.01	0.00	0.01	0.02	0.00	0.01	0.00	0.01	-0.00	0.00	-0.00	0.00	0.01	0.00	-0.01	0.01	-0.00	-0.01	0.02	0.00	-0.04	0.02	0.02	-0.01	-0.02
gender	0.06	1.00	-0.05	0.00	-0.01	-0.01	-0.02	0.00	0.07	-0.01	-0.01	-0.02	-0.00	-0.07	-0.02	-0.02	-0.00	-0.00	-0.02	0.00	-0.00	-0.00	0.00	-0.00	-0.00	0.03	0.03	0.00	0.01	0.01	0.01	0.00	0.01	0.00	0.00	0.00	0.00	-0.02	0.02	-0.00	0.05	-0.01	-0.02	0.00	-0.01	-0.03	-0.04	-0.01

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d <<<<<<< HEAD >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

	race	gender	age	admission_type_id	discharge_disposition_id	admission_source_id	time_in_hospital	num_lab_procedures	num_procedures	num_medications	number_outpatient	number_emergency	number_inpatient	diag_1	diag_2	diag_3	number_diagnoses	max_glu_serum	A1Cresult	metformin	repaglinide	nateglinide	chlorpropamide	glimepiride	acetohexamide	glipizide	glyburide	tolbutamide	pioglitazone	rosiglitazone	acarbose	miglitol	troglitazone	tolazamide	insulin	glyburide-metformin	glipizide-metformin	change	diabetesMed	readmitted	Diag_Circulatory	Diag_Respiratory	Diag_Digestive	Diag_Diabetes	Diag_Injury	Diag_Musculoskeletal	Diag_Genitourinary	Diag_Neoplasms
race	1.00	0.06	0.10	0.06	-0.01	0.06	-0.01	-0.01	0.03	0.03	0.03	-0.03	0.01	0.01	0.01	0.00	0.03	-0.01	-0.02	0.01	0.01	-0.01	0.00	0.01	0.00	0.01	0.02	0.00	0.01	0.00	0.01	-0.00	0.00	-0.00	0.00	0.01	0.00	-0.01	0.01	-0.00	-0.01	0.02	0.00	-0.04	0.02	0.02	-0.01	-0.02
gender	0.06	1.00	-0.05	0.00	-0.01	-0.01	-0.02	0.00	0.07	-0.01	-0.01	-0.02	-0.00	-0.07	-0.02	-0.02	-0.00	-0.00	-0.02	0.00	-0.00	-0.00	0.00	-0.00	-0.00	0.03	0.03	0.00	0.01	0.01	0.01	0.00	0.01	0.00	0.00	0.00	0.00	-0.02	0.02	-0.00	0.05	-0.01	-0.02	0.00	-0.01	-0.03	-0.04	-0.01
age	0.10	-0.05	1.00	0.01	0.11	-0.01	0.13	0.03	-0.03	0.05	0.03	-0.05	0.02	-0.02	-0.05	-0.10	0.25	0.00	0.07	-0.06	0.04	0.01	0.01	0.03	0.00	0.04	0.06	0.01	0.01	0.00	0.01	0.01	-0.00	0.01	-0.02	-0.00	0.00	0.04	-0.03	0.04	0.21	0.06	-0.03	-0.25	0.03	-0.00	0.09	-0.08
admission_type_id	0.06	0.00	0.01	1.00	-0.04	0.18	0.02	-0.05	0.01	-0.08	0.02	0.00	0.00	-0.01	0.01	0.00	-0.01	-0.01	-0.01	-0.02	0.02	-0.00	-0.01	0.01	-0.00	-0.00	0.00	0.00	-0.00	0.02	-0.01	0.01	-0.00	-0.00	0.03	-0.01	-0.01	-0.01	0.02	0.00	0.01	0.00	0.01	-0.01	-0.02	-0.05	-0.02	0.03
discharge_disposition_id	-0.01	-0.01	0.11	-0.04	1.00	0.00	0.16	0.05	0.02	0.10	-0.01	-0.02	0.00	-0.01	0.01	0.02	0.05	-0.02	0.00	-0.01	-0.01	-0.01	0.02	-0.02	0.00	-0.01	0.04	0.01	-0.03	-0.01	0.01	0.00	0.01	0.00	-0.03	-0.01	0.01	0.02	-0.04	0.03	0.02	0.00	-0.05	-0.04	0.03	0.04	0.00	0.01
admission_source_id	0.06	-0.01	-0.01	0.18	0.00	1.00	0.01	-0.18	0.19	0.10	0.00	-0.06	-0.02	-0.02	-0.03	-0.02	-0.14	0.02	0.02	0.01	-0.01	0.00	-0.00	0.02	-0.00	0.00	0.01	0.00	0.02	0.02	-0.00	-0.00	-0.01	-0.00	0.01	-0.00	-0.00	0.02	0.00	-0.01	0.03	-0.09	-0.05	0.01	0.01	0.13	-0.04	-0.06
time_in_hospital	-0.01	-0.02	0.13	0.02	0.16	0.01	1.00	0.33	0.19	0.47	-0.01	-0.01	0.06	0.02	0.07	0.05	0.23	-0.03	-0.03	-0.00	0.03	0.01	0.00	0.01	0.01	0.02	0.02	0.00	0.01	0.01	0.01	-0.00	0.00	-0.01	0.06	-0.00	0.00	-0.11	0.07	0.05	-0.03	0.02	0.01	-0.14	0.05	-0.02	0.05	0.05
num_lab_procedures	-0.01	0.00	0.03	-0.05	0.05	-0.18	0.33	1.00	0.05	0.26	-0.01	0.01	0.08	-0.04	0.03	0.03	0.16	0.00	-0.12	-0.04	0.01	-0.01	-0.00	-0.00	0.00	0.02	-0.00	-0.00	-0.01	-0.01	0.00	-0.00	0.00	-0.00	0.05	-0.01	-0.00	-0.07	0.04	0.03	-0.01	0.05	0.03	-0.07	-0.03	-0.12	0.10	0.04
num_procedures	0.03	0.07	-0.03	0.01	0.02	0.19	0.19	0.05	1.00	0.40	-0.02	-0.04	-0.02	-0.24	-0.12	-0.07	0.09	0.02	0.01	-0.04	0.00	-0.00	0.00	0.01	0.01	0.01	0.01	-0.00	0.01	0.01	0.00	0.00	-0.01	0.00	0.01	-0.00	-0.00	-0.01	-0.01	-0.00	0.15	-0.12	0.00	-0.10	0.08	0.02	-0.05	-0.16
num_medications	0.03	-0.01	0.05	-0.08	0.10	0.10	0.47	0.26	0.40	1.00	0.03	0.00	0.04	-0.05	-0.01	-0.02	0.26	-0.02	-0.01	0.07	0.02	0.02	0.00	0.04	0.01	0.06	0.04	0.00	0.07	0.05	0.02	0.00	0.00	-0.00	0.09	0.01	0.00	-0.25	0.19	0.03	0.09	0.05	-0.05	-0.14	0.09	0.09	0.01	-0.11
number_outpatient	0.03	-0.01	0.03	0.02	-0.01	0.00	-0.01	-0.01	-0.02	0.03	1.00	0.10	0.07	0.02	0.02	0.00	0.08	-0.01	0.00	0.00	-0.00	0.00	-0.01	-0.00	-0.00	0.01	0.00	-0.00	0.02	0.00	0.01	0.00	-0.00	-0.00	0.02	-0.00	0.00	-0.02	0.02	0.01	-0.00	-0.01	-0.00	-0.03	-0.01	0.01	0.02	0.01
number_emergency	-0.03	-0.02	-0.05	0.00	-0.02	-0.06	-0.01	0.01	-0.04	0.00	0.10	1.00	0.15	0.02	0.02	0.02	0.05	-0.01	0.01	-0.00	0.01	0.00	-0.00	0.01	-0.00	-0.00	-0.02	-0.00	0.01	0.00	0.00	0.00	-0.00	-0.00	0.01	0.01	-0.00	-0.03	0.03	0.03	-0.03	0.01	0.01	0.01	-0.01	-0.00	0.01	0.03
number_inpatient	0.01	-0.00	0.02	0.00	0.00	-0.02	0.06	0.08	-0.02	0.04	0.07	0.15	1.00	0.02	0.00	0.00	0.07	-0.01	0.02	-0.04	0.01	-0.00	-0.00	-0.00	-0.00	0.00	-0.01	-0.00	-0.01	-0.01	-0.01	-0.00	-0.00	-0.00	0.01	-0.01	-0.00	-0.00	0.02	0.10	0.01	0.00	-0.00	-0.03	0.01	-0.02	0.01	0.00
diag_1	0.01	-0.07	-0.02	-0.01	-0.01	-0.02	0.02	-0.04	-0.24	-0.05	0.02	0.02	0.02	1.00	0.14	0.07	0.01	-0.00	0.03	0.05	-0.01	0.00	-0.01	-0.01	0.01	-0.01	-0.01	-0.00	0.00	0.01	-0.00	-0.01	-0.00	-0.00	-0.01	0.01	-0.00	-0.00	-0.01	-0.02	-0.36	0.43	-0.09	-0.04	0.03	0.10	-0.01	0.18
diag_2	0.01	-0.02	-0.05	0.01	0.01	-0.03	0.07	0.03	-0.12	-0.01	0.02	0.02	0.00	0.14	1.00	0.08	0.08	-0.01	-0.00	0.03	-0.01	-0.00	-0.01	-0.01	0.01	-0.00	-0.01	-0.01	-0.00	-0.00	-0.01	0.01	0.00	-0.01	0.01	-0.00	-0.00	-0.02	0.01	-0.01	-0.43	0.28	-0.00	-0.13	0.06	0.03	-0.01	0.24
diag_3	0.00	-0.02	-0.10	0.00	0.02	-0.02	0.05	0.03	-0.07	-0.02	0.00	0.02	0.00	0.07	0.08	1.00	0.04	-0.01	-0.01	0.00	-0.01	-0.00	-0.02	-0.01	-0.00	-0.01	-0.02	0.00	-0.01	-0.00	-0.00	-0.00	-0.00	-0.00	0.02	0.01	0.01	-0.02	0.00	0.00	-0.42	0.18	-0.02	-0.16	0.05	0.01	0.00	0.26
number_diagnoses	0.03	-0.00	0.25	-0.01	0.05	-0.14	0.23	0.16	0.09	0.26	0.08	0.05	0.07	0.01	0.08	0.04	1.00	0.01	0.02	-0.06	0.03	0.02	-0.01	0.01	0.00	-0.01	-0.02	0.00	0.01	-0.01	0.01	-0.00	0.00	-0.01	0.03	-0.00	0.00	-0.05	0.02	0.04	0.06	0.09	0.01	-0.42	0.07	-0.07	0.14	0.02
max_glu_serum	-0.01	-0.00	0.00	-0.01	-0.02	0.02	-0.03	0.00	0.02	-0.02	-0.01	-0.01	-0.01	-0.00	-0.01	-0.01	0.01	1.00	0.00	-0.00	0.00	0.00	-0.00	0.01	0.00	-0.01	-0.01	-0.01	0.00	0.00	-0.01	0.00	0.00	-0.00	-0.01	0.00	0.00	0.04	-0.04	-0.00	0.02	-0.02	-0.01	-0.02	0.01	0.01	-0.00	-0.00
A1Cresult	-0.02	-0.02	0.07	-0.01	0.00	0.02	-0.03	-0.12	0.01	-0.01	0.00	0.01	0.02	0.03	-0.00	-0.01	0.02	0.00	1.00	-0.05	-0.02	0.00	0.00	-0.02	0.00	-0.02	-0.02	0.00	-0.00	-0.01	-0.01	-0.00	0.00	-0.00	-0.04	-0.00	-0.01	0.08	-0.07	0.00	0.01	-0.01	0.03	-0.08	0.03	0.01	0.00	-0.02
metformin	0.01	0.00	-0.06	-0.02	-0.01	0.01	-0.00	-0.04	-0.04	0.07	0.00	-0.00	-0.04	0.05	0.03	0.00	-0.06	-0.00	-0.05	1.00	0.01	0.02	-0.01	0.04	-0.00	0.06	0.12	-0.01	0.06	0.08	0.01	0.01	-0.00	0.00	0.01	-0.02	-0.00	-0.32	0.26	-0.01	-0.03	0.01	-0.05	0.06	0.01	0.07	-0.06	0.02
repaglinide	0.01	-0.00	0.04	0.02	-0.01	-0.01	0.03	0.01	0.00	0.02	-0.00	0.01	0.01	-0.01	-0.01	-0.01	0.03	0.00	-0.02	0.01	1.00	0.00	-0.00	-0.01	-0.00	-0.02	-0.01	-0.00	0.02	0.02	0.01	0.02	-0.00	-0.00	-0.00	-0.00	-0.00	-0.07	0.06	0.02	0.02	0.00	-0.02	-0.01	-0.01	-0.00	0.01	-0.00
nateglinide	-0.01	-0.00	0.01	-0.00	-0.01	0.00	0.01	-0.01	-0.00	0.02	0.00	0.00	-0.00	0.00	-0.00	-0.00	0.02	0.00	0.00	0.02	0.00	1.00	-0.00	0.01	-0.00	-0.01	-0.02	-0.00	0.03	0.01	0.00	0.02	-0.00	-0.00	-0.01	-0.01	-0.00	-0.05	0.04	-0.00	0.01	-0.00	-0.01	-0.01	-0.01	0.00	0.01	0.01
chlorpropamide	0.00	0.00	0.01	-0.01	0.02	-0.00	0.00	-0.00	0.00	0.00	-0.01	-0.00	-0.00	-0.01	-0.01	-0.02	-0.01	-0.00	0.00	-0.01	-0.00	-0.00	1.00	-0.01	-0.00	-0.01	-0.01	-0.00	-0.01	0.00	-0.00	-0.00	-0.00	-0.00	-0.01	-0.00	-0.00	0.01	0.02	-0.01	0.01	-0.00	-0.01	0.00	-0.00	0.01	-0.00	-0.01
glimepiride	0.01	-0.00	0.03	0.01	-0.02	0.02	0.01	-0.00	0.01	0.04	-0.00	0.01	-0.00	-0.01	-0.01	-0.01	0.01	0.01	-0.02	0.04	-0.01	0.01	-0.01	1.00	-0.00	-0.06	-0.06	-0.00	0.04	0.03	0.01	0.01	0.01	-0.00	0.01	-0.01	-0.00	-0.13	0.12	-0.00	0.03	0.01	-0.02	-0.01	0.00	0.00	-0.01	-0.01
acetohexamide	0.00	-0.00	0.00	-0.00	0.00	-0.00	0.01	0.00	0.01	0.01	-0.00	-0.00	-0.00	0.01	0.01	-0.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	1.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	0.00	-0.00	0.00	0.01	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00
glipizide	0.01	0.03	0.04	-0.00	-0.01	0.00	0.02	0.02	0.01	0.06	0.01	-0.00	0.00	-0.01	-0.00	-0.01	-0.01	-0.01	-0.02	0.06	-0.02	-0.01	-0.01	-0.06	-0.00	1.00	-0.08	-0.01	0.04	0.04	0.02	0.00	-0.00	-0.01	0.01	-0.03	-0.00	-0.18	0.19	0.01	0.02	0.01	-0.04	-0.00	-0.00	0.02	-0.01	-0.01
glyburide	0.02	0.03	0.06	0.00	0.04	0.01	0.02	-0.00	0.01	0.04	0.00	-0.02	-0.01	-0.01	-0.01	-0.02	-0.02	-0.01	-0.02	0.12	-0.01	-0.02	-0.01	-0.06	-0.00	-0.08	1.00	-0.00	0.02	0.03	0.02	-0.00	-0.00	-0.01	-0.02	-0.01	0.00	-0.17	0.17	0.01	0.04	0.01	-0.04	-0.01	-0.00	0.02	-0.02	-0.03
tolbutamide	0.00	0.00	0.01	0.00	0.01	0.00	0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.01	0.00	0.00	-0.01	0.00	-0.01	-0.00	-0.00	-0.00	-0.00	-0.00	-0.01	-0.00	1.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	0.01	-0.00	0.01	0.00	-0.00	-0.00	0.01	0.00	0.00	-0.01
pioglitazone	0.01	0.01	0.01	-0.00	-0.03	0.02	0.01	-0.01	0.01	0.07	0.02	0.01	-0.01	0.00	-0.00	-0.01	0.01	0.00	-0.00	0.06	0.02	0.03	-0.01	0.04	-0.00	0.04	0.02	-0.00	1.00	-0.06	0.01	0.00	-0.00	-0.00	0.00	0.02	0.00	-0.21	0.15	-0.00	0.01	-0.00	-0.03	0.00	0.02	0.03	-0.00	-0.01
rosiglitazone	0.00	0.01	0.00	0.02	-0.01	0.02	0.01	-0.01	0.01	0.05	0.00	0.00	-0.01	0.01	-0.00	-0.00	-0.01	0.00	-0.01	0.08	0.02	0.01	0.00	0.03	-0.00	0.04	0.03	-0.00	-0.06	1.00	0.01	0.00	0.01	0.00	0.01	0.01	-0.00	-0.20	0.14	0.00	0.01	0.01	-0.03	0.01	-0.00	0.02	-0.01	-0.00
acarbose	0.01	0.01	0.01	-0.01	0.01	-0.00	0.01	0.00	0.00	0.02	0.01	0.00	-0.01	-0.00	-0.01	-0.00	0.01	-0.01	-0.01	0.01	0.01	0.00	-0.00	0.01	-0.00	0.02	0.02	-0.00	0.01	0.01	1.00	-0.00	-0.00	-0.00	0.00	0.01	-0.00	-0.05	0.03	-0.00	0.01	-0.00	-0.01	-0.00	-0.00	0.00	0.00	-0.00
miglitol	-0.00	0.00	0.01	0.01	0.00	-0.00	-0.00	-0.00	0.00	0.00	0.00	0.00	-0.00	-0.01	0.01	-0.00	-0.00	0.00	-0.00	0.01	0.02	0.02	-0.00	0.01	-0.00	0.00	-0.00	-0.00	0.00	0.00	-0.00	1.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.01	0.01	-0.00	-0.00	0.00	0.00	0.01	-0.00	0.00	-0.00	-0.01
troglitazone	0.00	0.01	-0.00	-0.00	0.01	-0.01	0.00	0.00	-0.01	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.01	-0.00	-0.00	-0.00	-0.00	-0.00	0.01	-0.00	-0.00	1.00	-0.00	-0.00	-0.00	-0.00	-0.01	0.00	-0.00	0.00	0.00	-0.00	-0.00	0.01	0.01	-0.00	-0.01
tolazamide	-0.00	0.00	0.01	-0.00	0.00	-0.00	-0.01	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.01	-0.00	-0.01	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.01	-0.01	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	1.00	-0.00	-0.00	-0.00	0.00	0.01	-0.00	0.00	0.00	-0.00	-0.00	0.00	0.01	-0.01	-0.01
insulin	0.00	0.00	-0.02	0.03	-0.03	0.01	0.06	0.05	0.01	0.09	0.02	0.01	0.01	-0.01	0.01	0.02	0.03	-0.01	-0.04	0.01	-0.00	-0.01	-0.01	0.01	0.00	0.01	-0.02	-0.00	0.00	0.01	0.00	0.00	-0.00	-0.00	1.00	0.00	-0.00	-0.16	0.28	0.00	-0.02	0.01	0.00	0.01	0.00	-0.02	0.01	0.01
glyburide-metformin	0.01	0.00	-0.00	-0.01	-0.01	-0.00	-0.00	-0.01	-0.00	0.01	-0.00	0.01	-0.01	0.01	-0.00	0.01	-0.00	0.00	-0.00	-0.02	-0.00	-0.01	-0.00	-0.01	-0.00	-0.03	-0.01	-0.00	0.02	0.01	0.01	-0.00	-0.00	-0.00	0.00	1.00	0.03	-0.04	0.05	-0.00	-0.01	0.01	-0.01	0.00	0.01	0.02	-0.01	-0.00
glipizide-metformin	0.00	0.00	0.00	-0.01	0.01	-0.00	0.00	-0.00	-0.00	0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.01	0.00	0.00	-0.01	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	-0.00	-0.00	0.03	1.00	-0.01	0.01	-0.00	-0.00	-0.00	0.00	-0.00	0.01	-0.00	-0.00	-0.01
change	-0.01	-0.02	0.04	-0.01	0.02	0.02	-0.11	-0.07	-0.01	-0.25	-0.02	-0.03	-0.00	-0.00	-0.02	-0.02	-0.05	0.04	0.08	-0.32	-0.07	-0.05	0.01	-0.13	-0.00	-0.18	-0.17	0.00	-0.21	-0.20	-0.05	-0.01	-0.01	0.00	-0.16	-0.04	-0.01	1.00	-0.51	-0.02	0.02	-0.01	0.08	-0.07	-0.01	-0.02	0.01	-0.02
diabetesMed	0.01	0.02	-0.03	0.02	-0.04	0.00	0.07	0.04	-0.01	0.19	0.02	0.03	0.02	-0.01	0.01	0.00	0.02	-0.04	-0.07	0.26	0.06	0.04	0.02	0.12	0.00	0.19	0.17	0.01	0.15	0.14	0.03	0.01	0.00	0.01	0.28	0.05	0.01	-0.51	1.00	0.03	-0.01	0.01	-0.08	0.07	0.00	0.02	-0.00	0.00
readmitted	-0.00	-0.00	0.04	0.00	0.03	-0.01	0.05	0.03	-0.00	0.03	0.01	0.03	0.10	-0.02	-0.01	0.00	0.04	-0.00	0.00	-0.01	0.02	-0.00	-0.01	-0.00	-0.00	0.01	0.01	-0.00	-0.00	0.00	-0.00	-0.00	-0.00	-0.00	0.00	-0.00	-0.00	-0.02	0.03	1.00	0.02	-0.01	-0.01	-0.01	0.02	-0.01	0.01	0.00
Diag_Circulatory	-0.01	0.05	0.21	0.01	0.02	0.03	-0.03	-0.01	0.15	0.09	-0.00	-0.03	0.01	-0.36	-0.43	-0.42	0.06	0.02	0.01	-0.03	0.02	0.01	0.01	0.03	0.00	0.02	0.04	0.01	0.01	0.01	0.01	-0.00	0.00	0.00	-0.02	-0.01	-0.00	0.02	-0.01	0.02	1.00	-0.04	-0.19	-0.14	-0.12	-0.10	-0.13	-0.33
Diag_Respiratory	0.02	-0.01	0.06	0.00	0.00	-0.09	0.02	0.05	-0.12	0.05	-0.01	0.01	0.00	0.43	0.28	0.18	0.09	-0.02	-0.01	0.01	0.00	-0.00	-0.00	0.01	0.01	0.01	0.01	0.00	-0.00	0.01	-0.00	0.00	0.00	0.00	0.01	0.01	-0.00	-0.01	0.01	-0.01	-0.04	1.00	-0.10	-0.12	-0.10	-0.09	-0.10	-0.12
Diag_Digestive	0.00	-0.02	-0.03	0.01	-0.05	-0.05	0.01	0.03	0.00	-0.05	-0.00	0.01	-0.00	-0.09	-0.00	-0.02	0.01	-0.01	0.03	-0.05	-0.02	-0.01	-0.01	-0.02	-0.00	-0.04	-0.04	-0.00	-0.03	-0.03	-0.01	0.00	-0.00	-0.00	0.00	-0.01	0.00	0.08	-0.08	-0.01	-0.19	-0.10	1.00	-0.05	-0.07	-0.08	-0.05	-0.04
Diag_Diabetes	-0.04	0.00	-0.25	-0.01	-0.04	0.01	-0.14	-0.07	-0.10	-0.14	-0.03	0.01	-0.03	-0.04	-0.13	-0.16	-0.42	-0.02	-0.08	0.06	-0.01	-0.01	0.00	-0.01	-0.00	-0.00	-0.01	-0.00	0.00	0.01	-0.00	0.01	-0.00	-0.00	0.01	0.00	-0.00	-0.07	0.07	-0.01	-0.14	-0.12	-0.05	1.00	-0.11	0.04	-0.13	-0.04
Diag_Injury	0.02	-0.01	0.03	-0.02	0.03	0.01	0.05	-0.03	0.08	0.09	-0.01	-0.01	0.01	0.03	0.06	0.05	0.07	0.01	0.03	0.01	-0.01	-0.01	-0.00	0.00	-0.00	-0.00	-0.00	0.01	0.02	-0.00	-0.00	-0.00	0.01	0.00	0.00	0.01	0.01	-0.01	0.00	0.02	-0.12	-0.10	-0.07	-0.11	1.00	-0.02	-0.03	-0.07
Diag_Musculoskeletal	0.02	-0.03	-0.00	-0.05	0.04	0.13	-0.02	-0.12	0.02	0.09	0.01	-0.00	-0.02	0.10	0.03	0.01	-0.07	0.01	0.01	0.07	-0.00	0.00	0.01	0.00	-0.00	0.02	0.02	0.00	0.03	0.02	0.00	0.00	0.01	0.01	-0.02	0.02	-0.00	-0.02	0.02	-0.01	-0.10	-0.09	-0.08	0.04	-0.02	1.00	-0.06	-0.09
Diag_Genitourinary	-0.01	-0.04	0.09	-0.02	0.00	-0.04	0.05	0.10	-0.05	0.01	0.02	0.01	0.01	-0.01	-0.01	0.00	0.14	-0.00	0.00	-0.06	0.01	0.01	-0.00	-0.01	-0.00	-0.01	-0.02	0.00	-0.00	-0.01	0.00	-0.00	-0.00	-0.01	0.01	-0.01	-0.00	0.01	-0.00	0.01	-0.13	-0.10	-0.05	-0.13	-0.03	-0.06	1.00	0.01
Diag_Neoplasms	-0.02	-0.01	-0.08	0.03	0.01	-0.06	0.05	0.04	-0.16	-0.11	0.01	0.03	0.00	0.18	0.24	0.26	0.02	-0.00	-0.02	0.02	-0.00	0.01	-0.01	-0.01	-0.00	-0.01	-0.03	-0.01	-0.01	-0.00	-0.00	-0.01	-0.01	-0.01	0.01	-0.00	-0.01	-0.02	0.00	0.00	-0.33	-0.12	-0.04	-0.04	-0.07	-0.09	0.01	1.00	Diag_Neoplasms	-0.02	-0.01	-0.08	0.03	0.01	-0.06	0.05	0.04	-0.16	-0.11	0.01	0.03	0.00	0.18	0.24	0.26	0.02	-0.00	-0.02	0.02	-0.00	0.01	-0.01	-0.01	-0.00	-0.01	-0.03	-0.01	-0.01	-0.00	-0.00	-0.01	-0.01	-0.01	0.01	-0.00	-0.01	-0.02	0.00	0.00	-0.33	-0.12	-0.04	-0.04	-0.07	-0.09	0.01	1.00

We will retain here the variables that show the most correlation with other variables. There are the following :

• age
• admission_type_id
• discharge_disposition_id
• admission_source_id
• time_in_hospital
• num_lab_procedure
• num_procedures
• num_medication
• number_emergency
• number_inpatient
• diag_1
• diag_2
• diag_3
• number_diagnoses
• A1Cresult
• metformin
• glimepiride
• glipizide
• glyburide
• pioglitazone
• rosiglitazone
• insulin
• change
• diabeteMed
• Diag_Circulatory
• Diag_Respiratory
• Diag_Digestive
• Diag_Diabetes
• Diag_Injury
• Diag_Musculoskeletal
• Diag_Genitourinary
• Diag_Neoplasms

  There are more columns than the first time

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Now let's try to learn model on a dataset with the most correlated variables

new_diabetes_df = diabetes_df_ml.drop(columns = ["race", "gender", "number_outpatient", "diag_1", "diag_2", "diag_3", "number_emergency", "max_glu_serum", "repaglinide", "nateglinide", "chlorpropamide", "acetohexamide", "tolbutamide", "acarbose", "miglitol", "troglitazone", "tolazamide", "glyburide-metformin", "glipizide-metformin", ])

======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

Splitting the dataset

We import train_test_split from the sklearn library to separate our data into 2 groups in order to train our model and later verify the predictions

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from sklearn.model_selection import train_test_split

x = diabetes_df_ml.loc[:, diabetes_df_ml.columns != 'readmitted'] # every feature except the one that we will try to predict
y = diabetes_df_ml.loc[:, 'readmitted'] # readmitted feature
x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.33)

Scaling the dataset

We scale the data to have a same basis for all the columns

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from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)

Making predictions with Machine Learning models

The idea is to try several machine learning algorithms.

Then, we will compare them in order to see which one provides the best predictions

Let's create a dataframe to summarize the following results.

We will fill that dataframe with the score and accuracy of each model

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models_df = pd.DataFrame(columns=['Model', 'Score', 'Accuracy'])
pdtabulate = lambda df: tabulate(df, headers='keys', tablefmt='psql', floatfmt=".4f", showindex=False)
print(pdtabulate(models_df))

+---------+---------+------------+
| Model   | Score   | Accuracy   |
|---------+---------+------------|
+---------+---------+------------+

Libraries

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from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.metrics import classification_report 
from sklearn.metrics import confusion_matrix, accuracy_score

Model 1: K-Nearest Neighbors (KNN)

We again use the sklearn library to import knn

Creating model

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from sklearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier(n_neighbors = 5)
knn.fit(x_train, y_train)

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KNeighborsClassifier()

Computing predictions

We predict the readmitted feature using knn model applied on our test data

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y_pred = knn.predict(x_test)

Evaluating the performance of the model

Now we can build a confusion matrix and use the accuracy and score to have visibility on the performance of our model

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cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = knn.score(x_test,y_test)

We use the seaborn library to plot a beautiful confusion matrix

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ax = sns.heatmap(cm, annot=True, fmt='g', cmap="Blues", linewidths=5, cbar=False)  #annot=True to annotate cells, ftm='g' to disable scientific notation

# labels, title and ticks
ax.xaxis.set_label_position('top');
ax.xaxis.set_ticks_position('top');

ax.set_xlabel('Predicted labels', size = 20);ax.set_ylabel('True labels', size = 20); 
ax.set_title('Confusion Matrix', size = 30); 

ax.xaxis.set_ticklabels(['Readmitted < 30 days', 'Not readmitted'])
ax.yaxis.set_ticklabels(['Not readmitted', 'Readmitted < 30 days']);

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We can easily create a summary of our prediction with the classification report

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print(classification_report(y_test, y_pred))

              precision    recall  f1-score   support

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           0       0.91      0.99      0.95     21558
           1       0.13      0.01      0.02      2043

    accuracy                           0.91     23601
   macro avg       0.52      0.50      0.49     23601
=======
           0       0.91      0.99      0.95     21538
           1       0.17      0.02      0.03      2063

    accuracy                           0.91     23601
   macro avg       0.54      0.50      0.49     23601
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
           0       0.91      0.99      0.95     21545
           1       0.15      0.01      0.03      2056

    accuracy                           0.91     23601
   macro avg       0.53      0.50      0.49     23601
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d
weighted avg       0.85      0.91      0.87     23601

Adding results to the dataframe

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knn_sa = pd.DataFrame({'Model': ['K-Nearest Neighbors'],'Score': [sc], 'Accuracy' : [ac]})
models_df = models_df.append(knn_sa, ignore_index=True)

print(pdtabulate(models_df))

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
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| K-Nearest Neighbors |  0.9078 |     0.9078 |
=======
| K-Nearest Neighbors |  0.9067 |     0.9067 |
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
+---------------------+---------+------------+

Model 2: Logistic regression

Creating model

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

from sklearn.linear_model import LogisticRegression

logreg = LogisticRegression()
logreg.fit(x_train,y_train)

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

LogisticRegression()

Computing predictions

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

y_pred = logreg.predict(x_test)

Evaluating the performance of the model

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cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = logreg.score(x_test,y_test)

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

ax = sns.heatmap(cm, annot=True, fmt='g', cmap="Blues", linewidths=5, cbar=False)

ax.xaxis.set_label_position('top');
ax.xaxis.set_ticks_position('top');

ax.set_xlabel('Predicted labels', size = 20);ax.set_ylabel('True labels', size = 20); 
ax.set_title('Confusion Matrix', size = 30); 

ax.xaxis.set_ticklabels(['Readmitted < 30 days', 'Not readmitted'])
ax.yaxis.set_ticklabels(['Not readmitted', 'Readmitted < 30 days']);

<<<<<<< HEAD <<<<<<< HEAD >>>>>> 98d343efacd2506462a77b07e8ea11626b539aee =======

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

print(classification_report(y_test, y_pred))

              precision    recall  f1-score   support

<<<<<<< HEAD
<<<<<<< HEAD
           0       0.91      1.00      0.95     21558
           1       0.19      0.00      0.00      2043

    accuracy                           0.91     23601
   macro avg       0.55      0.50      0.48     23601
=======
           0       0.91      1.00      0.95     21538
           1       0.25      0.00      0.00      2063

    accuracy                           0.91     23601
   macro avg       0.58      0.50      0.48     23601
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
weighted avg       0.85      0.91      0.87     23601
=======
           0       0.91      1.00      0.95     21545
           1       0.46      0.00      0.01      2056

    accuracy                           0.91     23601
   macro avg       0.69      0.50      0.48     23601
weighted avg       0.87      0.91      0.87     23601
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

Adding results to the dataframe

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

logreg_sa = pd.DataFrame({'Model': ['Logistic Regression'],'Score': [sc], 'Accuracy' : [ac]})
models_df = models_df.append(logreg_sa, ignore_index=True)

print(pdtabulate(models_df))

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
<<<<<<< HEAD
| K-Nearest Neighbors |  0.9078 |     0.9078 |
| Logistic Regression |  0.9127 |     0.9127 |
=======
| K-Nearest Neighbors |  0.9067 |     0.9067 |
<<<<<<< HEAD
| Logistic Regression |  0.9122 |     0.9122 |
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
| Logistic Regression |  0.9128 |     0.9128 |
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d
+---------------------+---------+------------+

Model 3: Linear SVC

Creating model

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

from sklearn.svm import LinearSVC

lsvc  = LinearSVC( dual = False)
lsvc.fit(x_train, y_train)

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

LinearSVC(dual=False)

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

cv_score = cross_val_score(lsvc, x_train, y_train, cv = 10)
print(cv_score.mean())

<<<<<<< HEAD <<<<<<< HEAD

0.9113675596529246
=======
0.9117223611397135
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
0.9115971481028877
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

Computing predictions

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

y_pred = lsvc.predict(x_test)

Evaluating the performance of the model

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

ac = accuracy_score(y_test,y_pred)
sc = lsvc.score(x_test,y_test)
cm = confusion_matrix(y_test, y_pred)

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

ax = sns.heatmap(cm, annot=True, fmt='g', cmap="Blues", linewidths=5, cbar=False)

ax.xaxis.set_label_position('top');
ax.xaxis.set_ticks_position('top');

ax.set_xlabel('Predicted labels', size = 20);ax.set_ylabel('True labels', size = 20); 
ax.set_title('Confusion Matrix', size = 30); 

ax.xaxis.set_ticklabels(['Readmitted < 30 days', 'Not readmitted'])
ax.yaxis.set_ticklabels(['Not readmitted', 'Readmitted < 30 days']);

<<<<<<< HEAD <<<<<<< HEAD >>>>>> 98d343efacd2506462a77b07e8ea11626b539aee =======

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

print(classification_report(y_test, y_pred))

              precision    recall  f1-score   support

<<<<<<< HEAD
<<<<<<< HEAD
           0       0.91      1.00      0.95     21558
           1       0.17      0.00      0.00      2043

    accuracy                           0.91     23601
   macro avg       0.54      0.50      0.48     23601
weighted avg       0.85      0.91      0.87     23601
=======
           0       0.91      1.00      0.95     21538
           1       0.43      0.00      0.00      2063

    accuracy                           0.91     23601
   macro avg       0.67      0.50      0.48     23601
weighted avg       0.87      0.91      0.87     23601
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
           0       0.91      1.00      0.95     21545
           1       0.75      0.00      0.00      2056

    accuracy                           0.91     23601
   macro avg       0.83      0.50      0.48     23601
weighted avg       0.90      0.91      0.87     23601
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

Adding results to the dataframe

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

lsvc_sa = pd.DataFrame({'Model': ['Linear SVC'],'Score': [sc], 'Accuracy' : [ac]})
models_df = models_df.append(lsvc_sa, ignore_index=True)

print(pdtabulate(models_df))

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
<<<<<<< HEAD
| K-Nearest Neighbors |  0.9078 |     0.9078 |
| Logistic Regression |  0.9127 |     0.9127 |
| Linear SVC          |  0.9133 |     0.9133 |
=======
| K-Nearest Neighbors |  0.9067 |     0.9067 |
<<<<<<< HEAD
| Logistic Regression |  0.9122 |     0.9122 |
| Linear SVC          |  0.9125 |     0.9125 |
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
| Logistic Regression |  0.9128 |     0.9128 |
| Linear SVC          |  0.9130 |     0.9130 |
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d
+---------------------+---------+------------+

Model 4: Random Forest

Creating model

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

from sklearn.ensemble import RandomForestClassifier

clf = RandomForestClassifier(n_estimators = 10)
clf = clf.fit(x_train, y_train)

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

from sklearn.model_selection import cross_val_score
scores = cross_val_score( clf, x_train, y_train, cv = 5)
scores.mean()

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

<<<<<<< HEAD <<<<<<< HEAD

0.910637143595791

=======

0.9110127870601407

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee =======

0.9103867109901943

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

Computing predictions

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

y_pred = clf.predict(x_test)

Evaluating the model's performance

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = max(scores) # ???

Adding results to the dataframe

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

rf_sa = pd.DataFrame({'Model': ['Random Forest'],'Score': [sc], 'Accuracy' : [ac]})
models_df = models_df.append(rf_sa, ignore_index=True)

print(pdtabulate(models_df))

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
<<<<<<< HEAD
| K-Nearest Neighbors |  0.9078 |     0.9078 |
| Logistic Regression |  0.9127 |     0.9127 |
| Linear SVC          |  0.9133 |     0.9133 |
| Random Forest       |  0.9109 |     0.9127 |
=======
| K-Nearest Neighbors |  0.9067 |     0.9067 |
<<<<<<< HEAD
| Logistic Regression |  0.9122 |     0.9122 |
| Linear SVC          |  0.9125 |     0.9125 |
| Random Forest       |  0.9113 |     0.9117 |
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
| Logistic Regression |  0.9128 |     0.9128 |
| Linear SVC          |  0.9130 |     0.9130 |
| Random Forest       |  0.9109 |     0.9121 |
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d
+---------------------+---------+------------+

Model 5: Adaptive boosting

Creating model

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

from sklearn.ensemble import AdaBoostClassifier

ab = AdaBoostClassifier()
ab.fit(x_train, y_train)

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

AdaBoostClassifier()

Computing predictions

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

y_pred = ab.predict(x_test)

Evaluating the model's performance

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = ab.score(x_test,y_test)

Adding results to the dataframe

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

rf_sa = pd.DataFrame({'Model': ['Adaptive boosting'],'Score': [sc], 'Accuracy' : [ac]})
models_df = models_df.append(rf_sa, ignore_index=True)

print(pdtabulate(models_df))

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
<<<<<<< HEAD
| K-Nearest Neighbors |  0.9078 |     0.9078 |
| Logistic Regression |  0.9127 |     0.9127 |
| Linear SVC          |  0.9133 |     0.9133 |
| Random Forest       |  0.9109 |     0.9127 |
| Adaptive boosting   |  0.9127 |     0.9127 |
=======
| K-Nearest Neighbors |  0.9067 |     0.9067 |
<<<<<<< HEAD
| Logistic Regression |  0.9122 |     0.9122 |
| Linear SVC          |  0.9125 |     0.9125 |
| Random Forest       |  0.9113 |     0.9117 |
| Adaptive boosting   |  0.9123 |     0.9123 |
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
| Logistic Regression |  0.9128 |     0.9128 |
| Linear SVC          |  0.9130 |     0.9130 |
| Random Forest       |  0.9109 |     0.9121 |
| Adaptive boosting   |  0.9128 |     0.9128 |
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d
+---------------------+---------+------------+

Model 6: Decision Tree

Creating model

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

from sklearn.tree import DecisionTreeClassifier

clf = DecisionTreeClassifier(max_depth=None, min_samples_split=2, random_state=0)
scores = cross_val_score(clf, x_train, y_train, cv=5)
scores.mean()
clf.fit( x_train, y_train)

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

DecisionTreeClassifier(random_state=0)

Computing predictions

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

y_pred = clf.predict(x_test)

Evaluating the model's performance

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = max(scores) # ???

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

ax = sns.heatmap(cm, annot=True, fmt='g', cmap="Blues", linewidths=5, cbar=False)  #annot=True to annotate cells, ftm='g' to disable scientific notation

# labels, title and ticks
ax.xaxis.set_label_position('top');
ax.xaxis.set_ticks_position('top');

ax.set_xlabel('Predicted labels', size = 20);ax.set_ylabel('True labels', size = 20); 
ax.set_title('Confusion Matrix', size = 30); 

ax.xaxis.set_ticklabels(['Readmitted < 30 days', 'Not readmitted'])
ax.yaxis.set_ticklabels(['Not readmitted', 'Readmitted < 30 days']);

<<<<<<< HEAD <<<<<<< HEAD >>>>>> 98d343efacd2506462a77b07e8ea11626b539aee =======

Adding results to the dataframe

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

dt_sa = pd.DataFrame({'Model': ['Decision Tree'],'Score': [sc], 'Accuracy' : [ac]})
models_df = models_df.append(dt_sa, ignore_index=True)

print(pdtabulate(models_df))

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
<<<<<<< HEAD
| K-Nearest Neighbors |  0.9078 |     0.9078 |
| Logistic Regression |  0.9127 |     0.9127 |
| Linear SVC          |  0.9133 |     0.9133 |
| Random Forest       |  0.9109 |     0.9127 |
| Adaptive boosting   |  0.9127 |     0.9127 |
| Decision Tree       |  0.8315 |     0.8266 |
=======
| K-Nearest Neighbors |  0.9067 |     0.9067 |
<<<<<<< HEAD
| Logistic Regression |  0.9122 |     0.9122 |
| Linear SVC          |  0.9125 |     0.9125 |
| Random Forest       |  0.9113 |     0.9117 |
| Adaptive boosting   |  0.9123 |     0.9123 |
| Decision Tree       |  0.8338 |     0.8288 |
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
| Logistic Regression |  0.9128 |     0.9128 |
| Linear SVC          |  0.9130 |     0.9130 |
| Random Forest       |  0.9109 |     0.9121 |
| Adaptive boosting   |  0.9128 |     0.9128 |
| Decision Tree       |  0.8342 |     0.8327 |
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d
+---------------------+---------+------------+

Model 7: ExtraTrees

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

from sklearn.ensemble import ExtraTreesClassifier

clf = ExtraTreesClassifier(n_estimators=10, max_depth=None, min_samples_split=2, random_state=0)
scores = cross_val_score(clf, x_train, y_train, cv=5)
scores.mean()
clf.fit(x_train, y_train)

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

ExtraTreesClassifier(n_estimators=10, random_state=0)

Computing predictions

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

y_pred = clf.predict(x_test)

Evaluating the model's performance

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = max(scores) # ???

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

ax = sns.heatmap(cm, annot=True, fmt='g', cmap="Blues", linewidths=5, cbar=False)  #annot=True to annotate cells, ftm='g' to disable scientific notation

# labels, title and ticks
ax.xaxis.set_label_position('top');
ax.xaxis.set_ticks_position('top');

ax.set_xlabel('Predicted labels', size = 20);ax.set_ylabel('True labels', size = 20); 
ax.set_title('Confusion Matrix', size = 30); 

ax.xaxis.set_ticklabels(['Readmitted < 30 days', 'Not readmitted'])
ax.yaxis.set_ticklabels(['Not readmitted', 'Readmitted < 30 days']);

<<<<<<< HEAD <<<<<<< HEAD >>>>>> 98d343efacd2506462a77b07e8ea11626b539aee =======

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

print(classification_report(y_test, y_pred))

              precision    recall  f1-score   support

<<<<<<< HEAD
<<<<<<< HEAD
           0       0.91      1.00      0.95     21558
           1       0.12      0.00      0.00      2043

    accuracy                           0.91     23601
   macro avg       0.52      0.50      0.48     23601
weighted avg       0.84      0.91      0.87     23601
=======
           0       0.91      1.00      0.95     21538
           1       0.14      0.00      0.00      2063
=======
           0       0.91      1.00      0.95     21545
           1       0.15      0.00      0.01      2056
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

    accuracy                           0.91     23601
   macro avg       0.53      0.50      0.48     23601
weighted avg       0.85      0.91      0.87     23601
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

Adding results to the dataframe

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

et_sa = pd.DataFrame({'Model': ['Extra Trees'],'Score': [sc], 'Accuracy' : [ac]})
models_df = models_df.append(et_sa, ignore_index=True)

print(pdtabulate(models_df))

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
<<<<<<< HEAD
| K-Nearest Neighbors |  0.9078 |     0.9078 |
| Logistic Regression |  0.9127 |     0.9127 |
| Linear SVC          |  0.9133 |     0.9133 |
| Random Forest       |  0.9109 |     0.9127 |
| Adaptive boosting   |  0.9127 |     0.9127 |
| Decision Tree       |  0.8315 |     0.8266 |
| Extra Trees         |  0.9107 |     0.9121 |
=======
| K-Nearest Neighbors |  0.9067 |     0.9067 |
<<<<<<< HEAD
| Logistic Regression |  0.9122 |     0.9122 |
| Linear SVC          |  0.9125 |     0.9125 |
| Random Forest       |  0.9113 |     0.9117 |
| Adaptive boosting   |  0.9123 |     0.9123 |
| Decision Tree       |  0.8338 |     0.8288 |
| Extra Trees         |  0.9110 |     0.9115 |
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
| Logistic Regression |  0.9128 |     0.9128 |
| Linear SVC          |  0.9130 |     0.9130 |
| Random Forest       |  0.9109 |     0.9121 |
| Adaptive boosting   |  0.9128 |     0.9128 |
| Decision Tree       |  0.8342 |     0.8327 |
| Extra Trees         |  0.9112 |     0.9117 |
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d
+---------------------+---------+------------+

Model 8: Naive Bayes Classifier

Creating model

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

from sklearn.naive_bayes import GaussianNB

<<<<<<< HEAD
gnb = GaussianNB()
=======
gnb = GaussianNB(var_smoothing=1.0)
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
gnb.fit(x_train, y_train)

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

<<<<<<< HEAD

GaussianNB()

=======

GaussianNB(var_smoothing=1.0)

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

Calculating predictions

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

y_pred = gnb.predict(x_test)

Evaluating the performance of our model

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = gnb.score(x_test,y_test)

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

ax = sns.heatmap(cm, annot=True, fmt='g', cmap="Blues", linewidths=5, cbar=False)  #annot=True to annotate cells, ftm='g' to disable scientific notation

# labels, title and ticks
ax.xaxis.set_label_position('top');
ax.xaxis.set_ticks_position('top');

ax.set_xlabel('Predicted labels', size = 20);ax.set_ylabel('True labels', size = 20); 
ax.set_title('Confusion Matrix', size = 30); 

ax.xaxis.set_ticklabels(['Readmitted < 30 days', 'Not readmitted'])
ax.yaxis.set_ticklabels(['Not readmitted', 'Readmitted < 30 days']);

<<<<<<< HEAD <<<<<<< HEAD >>>>>> 98d343efacd2506462a77b07e8ea11626b539aee =======

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

print(classification_report(y_test, y_pred))

              precision    recall  f1-score   support

<<<<<<< HEAD
<<<<<<< HEAD
           0       0.96      0.00      0.01     21558
           1       0.09      1.00      0.16      2043

    accuracy                           0.09     23601
   macro avg       0.53      0.50      0.08     23601
weighted avg       0.89      0.09      0.02     23601
=======
           0       0.92      0.96      0.94     21538
           1       0.18      0.10      0.13      2063

    accuracy                           0.88     23601
   macro avg       0.55      0.53      0.53     23601
weighted avg       0.85      0.88      0.87     23601
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
           0       0.92      0.95      0.93     21545
           1       0.18      0.11      0.13      2056

    accuracy                           0.88     23601
   macro avg       0.55      0.53      0.53     23601
weighted avg       0.85      0.88      0.86     23601
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

Adding results to the dataframe

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

knn_sa = pd.DataFrame({'Model': ['Naive Bayes'],'Score': [sc], 'Accuracy' : [ac]})
models_df = models_df.append(knn_sa, ignore_index=True)

print(pdtabulate(models_df))

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
<<<<<<< HEAD
| K-Nearest Neighbors |  0.9078 |     0.9078 |
| Logistic Regression |  0.9127 |     0.9127 |
| Linear SVC          |  0.9133 |     0.9133 |
| Random Forest       |  0.9109 |     0.9127 |
| Adaptive boosting   |  0.9127 |     0.9127 |
| Decision Tree       |  0.8315 |     0.8266 |
| Extra Trees         |  0.9107 |     0.9121 |
| Naive Bayes         |  0.0888 |     0.0888 |
=======
| K-Nearest Neighbors |  0.9067 |     0.9067 |
<<<<<<< HEAD
| Logistic Regression |  0.9122 |     0.9122 |
| Linear SVC          |  0.9125 |     0.9125 |
| Random Forest       |  0.9113 |     0.9117 |
| Adaptive boosting   |  0.9123 |     0.9123 |
| Decision Tree       |  0.8338 |     0.8288 |
| Extra Trees         |  0.9110 |     0.9115 |
| Naive Bayes         |  0.8814 |     0.8814 |
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
| Logistic Regression |  0.9128 |     0.9128 |
| Linear SVC          |  0.9130 |     0.9130 |
| Random Forest       |  0.9109 |     0.9121 |
| Adaptive boosting   |  0.9128 |     0.9128 |
| Decision Tree       |  0.8342 |     0.8327 |
| Extra Trees         |  0.9112 |     0.9117 |
| Naive Bayes         |  0.8783 |     0.8783 |
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d
+---------------------+---------+------------+

<<<<<<< HEAD

Comparing models

Let's compare all different models we tried.

=======

Tuning models

from sklearn.model_selection import GridSearchCV

Let's try to find the best hyperparameters using GridSearch

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD

Performance of each model

=======

Grid Search with Adaptative Boosting

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD

print(pdtabulate(models_df.sort_values(by=['Accuracy', 'Score'], ascending = False)))
=======
parameters = { 'n_estimators': [100,200,300],
               'learning_rate': [0.95, 0.98, 1.0],
               'algorithm': ['SAMME', 'SAMME.R']}
grid = GridSearchCV(AdaBoostClassifier(), parameters, n_jobs = -1, cv = 3)
grid.fit(x_train,y_train)
y_pred = grid.predict(x_test)
ac = accuracy_score(y_test, y_pred)
sc = grid.best_score_
print(grid.best_estimator_, grid.best_params_)
print(sc, ac)
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee


     

<<<<<<< HEAD

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
| Linear SVC          |  0.9133 |     0.9133 |
| Logistic Regression |  0.9127 |     0.9127 |
| Adaptive boosting   |  0.9127 |     0.9127 |
| Random Forest       |  0.9109 |     0.9127 |
| Extra Trees         |  0.9107 |     0.9121 |
| K-Nearest Neighbors |  0.9078 |     0.9078 |
| Decision Tree       |  0.8315 |     0.8266 |
| Naive Bayes         |  0.0888 |     0.0888 |
+---------------------+---------+------------+
=======
AdaBoostClassifier(algorithm='SAMME', learning_rate=0.95, n_estimators=100) {'algorithm': 'SAMME', 'learning_rate': 0.95, 'n_estimators': 100}
<<<<<<< HEAD
0.9117223531589095 0.9125884496419643
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
0.9115762680895578 0.9128850472437608
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

<<<<<<< HEAD

Plot

=======

The best parameters to use with the AdaBoostClassifier are n_estimators = 300, learning_rate = 0.98. The score of the model is a little bit improved but it's not enormous.

We can update the dataframe with the new values

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD

bp = models_df[['Model', 'Accuracy']].groupby('Model').sum().plot_bokeh(kind = 'scatter',legend = False, title = 'Accuracy by model', line_cap = 'round',line_dash = 'dotted', show_figure = False)
bp.xaxis.major_label_orientation = np.pi/6
show(bp)
=======
models_df.loc[4] = ['Adaptive boosting', sc, ac]


     

Grid Search with Random Forest

parameters = {"n_estimators"      : [10,20,50, 100],
              "max_features"      : ["auto", "sqrt", "log2"],
              "min_samples_split" : [2,4,8],
              "bootstrap"         :[True, False],
              }

grid = GridSearchCV(RandomForestClassifier(), parameters, n_jobs = -1, cv = 3)
grid.fit(x_train,y_train)
y_pred = grid.predict(x_test)
ac = accuracy_score(y_test, y_pred)
sc = grid.best_score_
print(grid.best_estimator_, grid.best_params_)
print(sc, ac)
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD

=======

RandomForestClassifier(bootstrap=False, min_samples_split=4, n_estimators=50) {'bootstrap': False, 'max_features': 'auto', 'min_samples_split': 4, 'n_estimators': 50}
0.9116597475118802 0.912757933985848

We can update the dataframe with the new values

models_df.loc[3] = ['Random Forest', sc, ac]

Grid Search with Naives Bayes

nb_classifier = GaussianNB()

params_NB = {'var_smoothing': np.logspace(0,-9, num=100)}
grid = GridSearchCV(estimator=nb_classifier, 
                 param_grid=params_NB, 
                 cv=5,   # use any cross validation technique 
                 verbose=1, 
                 scoring='accuracy') 
grid.fit(x_train, y_train)
y_pred = grid.predict(x_test)
ac = accuracy_score(y_test, y_pred)
sc = grid.best_score_
print(grid.best_estimator_, grid.best_params_)
print(sc, ac)

Fitting 5 folds for each of 100 candidates, totalling 500 fits
GaussianNB(var_smoothing=1.0) {'var_smoothing': 1.0}
0.8865328457965962 0.8783102410914791

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD =======

We can update the dataframe with the new values

models_df.loc[7] = ['Naive Bayes', sc, ac]

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD

Barplot

=======

Grid Search with Extra Trees

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD <<<<<<< HEAD

from bokeh.models import Range1d
from bokeh.models import Span

bp = models_df.groupby('Model').sum().plot_bokeh(kind = 'barh', title = 'Accuracy and score by model',show_figure = False)
bp.x_range = Range1d(-0.1,1.4)

meanAc = Span(location = models_df.Accuracy.mean(), dimension = 'height', line_color = 'crimson', line_width = 1)
meanSc = Span(location = models_df.Score.mean(), dimension = 'height', line_color = 'blue', line_width = 1)
limit = Span(location = 1, dimension = 'height', line_color = 'black', line_width = 3)

bp.renderers.extend([limit, meanAc, meanSc])
show(bp)
=======

=======

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

     

from sklearn.model_selection import GridSearchCV

parameters = {"n_estimators"      : [10,20,50,100]}

grid = GridSearchCV(ExtraTreesClassifier(), parameters, n_jobs = -1, cv = 3)
grid.fit(x_train,y_train)
y_pred = grid.predict(x_test)
ac = accuracy_score(y_test, y_pred)
sc = grid.best_score_
print(grid.best_estimator_, grid.best_params_)
print(sc, ac)
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee


     

<<<<<<< HEAD

=======

ExtraTreesClassifier() {'n_estimators': 100}
0.9114927912803771 0.9125037074700224

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD

Finding the best hyperparameters

from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(n_estimators=100, max_depth=None, min_samples_split=2, random_state=0)
scores = cross_val_score(clf, x_train, y_train, cv=5)
scores.mean()
clf.fit( x_train, y_train)
=======
We can update the dataframe with the new values

models_df.loc[6] = ['Extra Trees', sc, ac]
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD

RandomForestClassifier(random_state=0)

from sklearn.model_selection import GridSearchCV

parameters = { 'n_estimators' : [100, 120, 150]}
#parameters = { 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, None],
#              'max_features': ['auto', 'sqrt'],
#              'min_samples_leaf': [1, 2, 4],
#              'min_samples_split': [2, 5, 10],
#              'n_estimators': [100,200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000]}
grid = GridSearchCV(RandomForestClassifier(), parameters, n_jobs = -1, cv = 5)
grid.fit(x_train,y_train)
sc = grid.best_score_
print(sc, grid.best_estimator_)
=======

Comparing models

Let's compare all different models we tried.

Performance of each model

print(pdtabulate(models_df.sort_values(by=['Score', 'Accuracy'], ascending = False)))
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD

0.9112632283762097 RandomForestClassifier(n_estimators=120)
=======
+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
| Linear SVC          |  0.9130 |     0.9130 |
| Logistic Regression |  0.9128 |     0.9128 |
| Random Forest       |  0.9117 |     0.9128 |
| Adaptive boosting   |  0.9116 |     0.9129 |
| Extra Trees         |  0.9115 |     0.9125 |
| K-Nearest Neighbors |  0.9067 |     0.9067 |
| Naive Bayes         |  0.8865 |     0.8783 |
| Decision Tree       |  0.8342 |     0.8327 |
+---------------------+---------+------------+
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD

ypred3 = clf.predict(x_test)
cm = confusion_matrix(y_test, ypred3)
ac = accuracy_score(y_test,ypred3)
cm,ac
=======

Plot

bp = models_df[['Model', 'Accuracy']].groupby('Model').sum().plot_bokeh(kind = 'scatter',legend = False, title = 'Accuracy by model', line_cap = 'round',line_dash = 'dotted', show_figure = False)
bp.xaxis.major_label_orientation = np.pi/6
show(bp)
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD

(array([[21557,     1],
        [ 2043,     0]], dtype=int64),
 0.9133935002754121)

<<<<<<< HEAD ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

count_0 = 0
count_1 = 1 
for rows in ypred3 :
    if rows == 0 : count_0 += 1
    else : count_1 += 1
print(count_0, count_1)

=======

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD

23600 2

=======

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD

As a result, we can conclude that the models predict the data relatively well since the accuracy is around 90%. However, this data is to be nuanced. Looking at the distribution of 0 and 1 data in the readmitted column, we can observe this:

=======

Barplot

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD <<<<<<< HEAD

p = diabetes_df_ml['readmitted'].value_counts(normalize = True).sort_values(ascending=False).plot_bokeh(  kind="bar", 
                                                                                title = 'Readmitted',  
                                                                                fill_alpha = 0.6, 
                                                                                legend = False, 
                                                                                show_figure=False)
p.xaxis.axis_label = '' # hide x label
p.yaxis.axis_label = '' # hide y label
p.xaxis.major_label_orientation = np.pi/6 # rotate bar labels to improve visibility

show(p)
=======

=======

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

     

from bokeh.models import Range1d
from bokeh.models import Span

bp = models_df.groupby('Model').sum().plot_bokeh(kind = 'barh', title = 'Accuracy and score by model',show_figure = False)
bp.x_range = Range1d(-0.1,1.4)

meanAc = Span(location = models_df.Accuracy.mean(), dimension = 'height', line_color = 'crimson', line_width = 1)
meanSc = Span(location = models_df.Score.mean(), dimension = 'height', line_color = 'blue', line_width = 1)
limit = Span(location = 1, dimension = 'height', line_color = 'black', line_width = 3)

bp.renderers.extend([limit, meanAc, meanSc])
show(bp)
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee


     

<<<<<<< HEAD <<<<<<< HEAD

=======

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee =======

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

<<<<<<< HEAD <<<<<<< HEAD

<<<<<<< HEAD

There are only 9.6% of 1 label against 90.4% of 0 label. The problem is also unbalanced.

In this case, we cannot really consider these prediction models reliable.

We will repeat the same procedure by predicting whether or not a patient can be readmitted, either before or after 30 days.

=======

As a result, we can conclude that the models predict the data relatively well since the accuracy is around 90%. However, this data is to be nuanced. Looking at the distribution of 0 and 1 data in the readmitted column, we can observe this:

p = diabetes_df_ml['readmitted'].value_counts(normalize = True).sort_values(ascending=False).plot_bokeh(  kind="bar", 
                                                                                title = 'Readmitted',  
                                                                                fill_alpha = 0.6, 
                                                                                legend = False, 
                                                                                show_figure=False)
p.xaxis.axis_label = '' # hide x label
p.yaxis.axis_label = '' # hide y label
p.xaxis.major_label_orientation = np.pi/6 # rotate bar labels to improve visibility

show(p)

There are only 8.8% of 1 label against 91.2% of 0 label. The problem is also unbalanced.

In this case, we cannot really consider these prediction models reliable.

We will repeat the same procedure by predicting whether or not a patient can be readmitted, either before or after 30 days.

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

Case 2 : Predict patient's readmission under and above 30 days

Dealing again with the column to predict

We will now consider the question "Will a patient be readmitted below OR above 30 days ?"

This involve that the 'NO' value will become False and the '<30' value AND the '<30' value will become True

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

diabetes_df_ml = diabetes_df.drop(columns = ['patient_nbr']).copy()
map_readmitted = {"NO" : False,
                  ">30" : True,
                  "<30" : True}

diabetes_df_ml.readmitted = diabetes_df.readmitted.map(map_readmitted)

Let's see the new repartition of the data in this column

<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

p = diabetes_df_ml['readmitted'].value_counts(normalize = True).sort_values(ascending=False).plot_bokeh(  kind="bar", 
                                                                                title = 'Readmitted',  
                                                                                fill_alpha = 0.6, 
                                                                                legend = False, 
                                                                                show_figure=False)
p.xaxis.axis_label = '' # hide x label
p.yaxis.axis_label = '' # hide y label
p.xaxis.major_label_orientation = np.pi/6 # rotate bar labels to improve visibility

show(p)

<<<<<<< HEAD <<<<<<< HEAD

=======

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee =======

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

<<<<<<< HEAD <<<<<<< HEAD

<<<<<<< HEAD

This is better. Let's do the precedents steps with this new dataset before using Machine Learning models

=======

This is better. Let's do the precedents steps with this new dataset before using Machine Learning models

#change to numerical values for all the columns 
from sklearn.preprocessing import LabelEncoder

label_encoder = LabelEncoder()

diabetes_df_ml["race"] = label_encoder.fit_transform(diabetes_df_ml["race"].astype(str))
#diabetes_df_ml["readmitted"] = label_encoder.fit_transform(diabetes_df_ml["readmitted"].astype(str))

for column in diabetes_df_ml.select_dtypes(include=['object']).columns:
    diabetes_df_ml[column] = label_encoder.fit_transform(diabetes_df_ml[column])
    
for column in diabetes_df_ml.select_dtypes(include=['bool']).columns:
    diabetes_df_ml[column] = label_encoder.fit_transform(diabetes_df_ml[column])
    
#shuffle the rows 
diabetes_df_ml = diabetes_df_ml.sample(n = len(diabetes_df_ml), random_state = 42)

#splitting the dataset
from sklearn.model_selection import train_test_split

x = diabetes_df_ml.loc[:, diabetes_df_ml.columns != 'readmitted'] # every feature except the one that we will try to predict
y = diabetes_df_ml.loc[:, 'readmitted'] # readmitted feature
x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.33)

#scaling the dataset
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)

#results modelisation
models_df2 = pd.DataFrame(columns=['Model', 'Score', 'Accuracy'])
pdtabulate2 = lambda df: tabulate(df, headers='keys', tablefmt='psql', floatfmt=".4f", showindex=False)
print(pdtabulate2(models_df2))

+---------+---------+------------+
| Model   | Score   | Accuracy   |
|---------+---------+------------|
+---------+---------+------------+

Model 1: K-Nearest Neighbors (KNN)

knn = KNeighborsClassifier(n_neighbors = 5)
knn.fit(x_train, y_train)

## Computing predictions
y_pred = knn.predict(x_test)

## Evaluating the performance of the model
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = knn.score(x_test,y_test)

ax = sns.heatmap(cm, annot=True, fmt='g', cmap="Blues", linewidths=5, cbar=False)  #annot=True to annotate cells, ftm='g' to disable scientific notation

# labels, title and ticks
ax.xaxis.set_label_position('top');
ax.xaxis.set_ticks_position('top');

ax.set_xlabel('Predicted labels', size = 20);ax.set_ylabel('True labels', size = 20); 
ax.set_title('Confusion Matrix', size = 30); 

ax.xaxis.set_ticklabels(['Not readmitted', 'Readmitted'])
ax.yaxis.set_ticklabels(['Not readmitted', 'Readmitted']);

print(classification_report(y_test, y_pred))

## Adding results to the dataframe
knn_sa = pd.DataFrame({'Model': ['K-Nearest Neighbors'],'Score': [sc], 'Accuracy' : [ac]})
models_df2 = models_df2.append(knn_sa, ignore_index=True)

print(pdtabulate2(models_df2))

              precision    recall  f1-score   support

           0       0.63      0.72      0.67     14125
           1       0.47      0.37      0.42      9476

    accuracy                           0.58     23601
   macro avg       0.55      0.55      0.54     23601
weighted avg       0.57      0.58      0.57     23601

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
| K-Nearest Neighbors |  0.5805 |     0.5805 |
+---------------------+---------+------------+

Model 2: Logistic regression

## Creating model
logreg = LogisticRegression()
logreg.fit(x_train,y_train)

## Computing predictions
y_pred = logreg.predict(x_test)

## Evaluating the performance of the model
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = logreg.score(x_test,y_test)

ax = sns.heatmap(cm, annot=True, fmt='g', cmap="Blues", linewidths=5, cbar=False)

ax.xaxis.set_label_position('top');
ax.xaxis.set_ticks_position('top');

ax.set_xlabel('Predicted labels', size = 20);ax.set_ylabel('True labels', size = 20); 
ax.set_title('Confusion Matrix', size = 30); 

ax.xaxis.set_ticklabels(['Not readmitted', 'Readmitted'])
ax.yaxis.set_ticklabels(['Not readmitted', 'Readmitted']);

print(classification_report(y_test, y_pred))

## Adding results to the dataframe

logreg_sa = pd.DataFrame({'Model': ['Logistic Regression'],'Score': [sc], 'Accuracy' : [ac]})
models_df2 = models_df2.append(logreg_sa, ignore_index=True)

print(pdtabulate2(models_df2))

              precision    recall  f1-score   support

           0       0.63      0.90      0.74     14125
           1       0.59      0.21      0.31      9476

    accuracy                           0.62     23601
   macro avg       0.61      0.56      0.53     23601
weighted avg       0.61      0.62      0.57     23601

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
| K-Nearest Neighbors |  0.5805 |     0.5805 |
| Logistic Regression |  0.6238 |     0.6238 |
+---------------------+---------+------------+

Model 3: Linear SVC

## Creating model
lsvc  = LinearSVC( dual = False)
lsvc.fit(x_train, y_train)

cv_score = cross_val_score(lsvc, x_train, y_train, cv = 10)
print(cv_score.mean())

## Computing predictions
y_pred = lsvc.predict(x_test)

## Evaluating the performance of the model
ac = accuracy_score(y_test,y_pred)
sc = lsvc.score(x_test,y_test)
cm = confusion_matrix(y_test, y_pred)

ax = sns.heatmap(cm, annot=True, fmt='g', cmap="Blues", linewidths=5, cbar=False)

ax.xaxis.set_label_position('top');
ax.xaxis.set_ticks_position('top');

ax.set_xlabel('Predicted labels', size = 20);ax.set_ylabel('True labels', size = 20); 
ax.set_title('Confusion Matrix', size = 30); 

ax.xaxis.set_ticklabels(['Not readmitted', 'Readmitted'])
ax.yaxis.set_ticklabels(['Not readmitted', 'Readmitted']);

print(classification_report(y_test, y_pred))

## Adding results to the dataframe
lsvc_sa = pd.DataFrame({'Model': ['Linear SVC'],'Score': [sc], 'Accuracy' : [ac]})
models_df2 = models_df2.append(lsvc_sa, ignore_index=True)

print(pdtabulate2(models_df2))

0.6261867035402007
              precision    recall  f1-score   support

           0       0.63      0.91      0.74     14125
           1       0.59      0.20      0.29      9476

    accuracy                           0.62     23601
   macro avg       0.61      0.55      0.52     23601
weighted avg       0.61      0.62      0.56     23601

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
| K-Nearest Neighbors |  0.5805 |     0.5805 |
| Logistic Regression |  0.6238 |     0.6238 |
| Linear SVC          |  0.6231 |     0.6231 |
+---------------------+---------+------------+

Model 4: Random Forest

## Creating model
clf = RandomForestClassifier(n_estimators = 10)
clf = clf.fit(x_train, y_train)

scores = cross_val_score( clf, x_train, y_train, cv = 5)
scores.mean()

## Computing predictions
y_pred = clf.predict(x_test)

## Evaluating the model's performance
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = max(scores) # ???

## Adding results to the dataframe
rf_sa = pd.DataFrame({'Model': ['Random Forest'],'Score': [sc], 'Accuracy' : [ac]})
models_df2 = models_df2.append(rf_sa, ignore_index=True)

print(pdtabulate2(models_df2))

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
| K-Nearest Neighbors |  0.5805 |     0.5805 |
| Logistic Regression |  0.6238 |     0.6238 |
| Linear SVC          |  0.6231 |     0.6231 |
| Random Forest       |  0.6093 |     0.6065 |
+---------------------+---------+------------+

Model 5: Adaptive boosting

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD <<<<<<< HEAD

#change to numerical values for all the columns 
from sklearn.preprocessing import LabelEncoder

label_encoder = LabelEncoder()

diabetes_df_ml["race"] = label_encoder.fit_transform(diabetes_df_ml["race"].astype(str))
#diabetes_df_ml["readmitted"] = label_encoder.fit_transform(diabetes_df_ml["readmitted"].astype(str))

for column in diabetes_df_ml.select_dtypes(include=['object']).columns:
    diabetes_df_ml[column] = label_encoder.fit_transform(diabetes_df_ml[column])
    
for column in diabetes_df_ml.select_dtypes(include=['bool']).columns:
    diabetes_df_ml[column] = label_encoder.fit_transform(diabetes_df_ml[column])
    
#shuffle the rows 
diabetes_df_ml = diabetes_df_ml.sample(n = len(diabetes_df_ml), random_state = 42)

#splitting the dataset
from sklearn.model_selection import train_test_split

x = diabetes_df_ml.loc[:, diabetes_df_ml.columns != 'readmitted'] # every feature except the one that we will try to predict
y = diabetes_df_ml.loc[:, 'readmitted'] # readmitted feature
x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.33)

#scaling the dataset
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)

#results modelisation
models_df2 = pd.DataFrame(columns=['Model', 'Score', 'Accuracy'])
pdtabulate2 = lambda df: tabulate(df, headers='keys', tablefmt='psql', floatfmt=".4f", showindex=False)
=======

=======

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

     

## Creating model
ab = AdaBoostClassifier()
ab.fit(x_train, y_train)

## Computing predictions
y_pred = ab.predict(x_test)

## Evaluating the model's performance
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = ab.score(x_test,y_test)

## Adding results to the dataframe
rf_sa = pd.DataFrame({'Model': ['Adaptive boosting'],'Score': [sc], 'Accuracy' : [ac]})
models_df2 = models_df2.append(rf_sa, ignore_index=True)

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
print(pdtabulate2(models_df2))


     

<<<<<<< HEAD

+---------+---------+------------+
| Model   | Score   | Accuracy   |
|---------+---------+------------|
+---------+---------+------------+
=======
+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
| K-Nearest Neighbors |  0.5805 |     0.5805 |
| Logistic Regression |  0.6238 |     0.6238 |
| Linear SVC          |  0.6231 |     0.6231 |
| Random Forest       |  0.6093 |     0.6065 |
| Adaptive boosting   |  0.6323 |     0.6323 |
+---------------------+---------+------------+
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD

Model 1: K-Nearest Neighbors (KNN)

=======

Model 6: Decision Tree

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD <<<<<<< HEAD

knn = KNeighborsClassifier(n_neighbors = 5)
knn.fit(x_train, y_train)

## Computing predictions
y_pred = knn.predict(x_test)

## Evaluating the performance of the model
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = knn.score(x_test,y_test)
=======

=======

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

     

## Creating model
clf = DecisionTreeClassifier(max_depth=None, min_samples_split=2, random_state=0)
scores = cross_val_score(clf, x_train, y_train, cv=5)
scores.mean()
clf.fit( x_train, y_train)

## Computing predictions
y_pred = clf.predict(x_test)

## Evaluating the model's performance
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = max(scores) # ???
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

ax = sns.heatmap(cm, annot=True, fmt='g', cmap="Blues", linewidths=5, cbar=False)  #annot=True to annotate cells, ftm='g' to disable scientific notation

# labels, title and ticks
ax.xaxis.set_label_position('top');
ax.xaxis.set_ticks_position('top');

ax.set_xlabel('Predicted labels', size = 20);ax.set_ylabel('True labels', size = 20); 
ax.set_title('Confusion Matrix', size = 30); 

ax.xaxis.set_ticklabels(['Not readmitted', 'Readmitted'])
ax.yaxis.set_ticklabels(['Not readmitted', 'Readmitted']);

<<<<<<< HEAD
print(classification_report(y_test, y_pred))

## Adding results to the dataframe
knn_sa = pd.DataFrame({'Model': ['K-Nearest Neighbors'],'Score': [sc], 'Accuracy' : [ac]})
models_df2 = models_df2.append(knn_sa, ignore_index=True)
=======
## Adding results to the dataframe
dt_sa = pd.DataFrame({'Model': ['Decision Tree'],'Score': [sc], 'Accuracy' : [ac]})
models_df2 = models_df2.append(dt_sa, ignore_index=True)
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

print(pdtabulate2(models_df2))


     

<<<<<<< HEAD

              precision    recall  f1-score   support

           0       0.63      0.72      0.67     14143
           1       0.47      0.37      0.41      9458

    accuracy                           0.58     23601
   macro avg       0.55      0.54      0.54     23601
weighted avg       0.56      0.58      0.57     23601

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
| K-Nearest Neighbors |  0.5777 |     0.5777 |
=======
+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
<<<<<<< HEAD
| K-Nearest Neighbors |  0.5756 |     0.5756 |
| Logistic Regression |  0.6241 |     0.6241 |
| Linear SVC          |  0.6246 |     0.6246 |
| Random Forest       |  0.6110 |     0.6073 |
| Adaptive boosting   |  0.6335 |     0.6335 |
| Decision Tree       |  0.5702 |     0.5604 |
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
| K-Nearest Neighbors |  0.5805 |     0.5805 |
| Logistic Regression |  0.6238 |     0.6238 |
| Linear SVC          |  0.6231 |     0.6231 |
| Random Forest       |  0.6093 |     0.6065 |
| Adaptive boosting   |  0.6323 |     0.6323 |
| Decision Tree       |  0.5600 |     0.5575 |
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d
+---------------------+---------+------------+

<<<<<<< HEAD <<<<<<< HEAD >>>>>> 98d343efacd2506462a77b07e8ea11626b539aee =======

<<<<<<< HEAD

Model 2: Logistic regression

=======

Model 7: ExtraTrees

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD <<<<<<< HEAD

## Creating model
logreg = LogisticRegression()
logreg.fit(x_train,y_train)

## Computing predictions
y_pred = logreg.predict(x_test)

## Evaluating the performance of the model
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = logreg.score(x_test,y_test)

ax = sns.heatmap(cm, annot=True, fmt='g', cmap="Blues", linewidths=5, cbar=False)

=======

=======

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

     

##Creating model 
clf = ExtraTreesClassifier(n_estimators=10, max_depth=None, min_samples_split=2, random_state=0)
scores = cross_val_score(clf, x_train, y_train, cv=5)
scores.mean()
clf.fit(x_train, y_train)

## Computing predictions
y_pred = clf.predict(x_test)

## Evaluating the model's performance
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = max(scores) # ???

ax = sns.heatmap(cm, annot=True, fmt='g', cmap="Blues", linewidths=5, cbar=False)  #annot=True to annotate cells, ftm='g' to disable scientific notation

# labels, title and ticks
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
ax.xaxis.set_label_position('top');
ax.xaxis.set_ticks_position('top');

ax.set_xlabel('Predicted labels', size = 20);ax.set_ylabel('True labels', size = 20); 
ax.set_title('Confusion Matrix', size = 30); 

ax.xaxis.set_ticklabels(['Not readmitted', 'Readmitted'])
ax.yaxis.set_ticklabels(['Not readmitted', 'Readmitted']);

print(classification_report(y_test, y_pred))

## Adding results to the dataframe
<<<<<<< HEAD

logreg_sa = pd.DataFrame({'Model': ['Logistic Regression'],'Score': [sc], 'Accuracy' : [ac]})
models_df2 = models_df2.append(logreg_sa, ignore_index=True)

print(pdtabulate2(models_df2))
=======
et_sa = pd.DataFrame({'Model': ['Extra Trees'],'Score': [sc], 'Accuracy' : [ac]})
models_df2 = models_df2.append(et_sa, ignore_index=True)

print(pdtabulate(models_df2))
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee


     

              precision    recall  f1-score   support

<<<<<<< HEAD
<<<<<<< HEAD
           0       0.63      0.91      0.75     14143
           1       0.60      0.20      0.30      9458

    accuracy                           0.63     23601
   macro avg       0.62      0.56      0.52     23601
weighted avg       0.62      0.63      0.57     23601
=======
           0       0.63      0.81      0.71     14240
           1       0.50      0.29      0.36      9361
=======
           0       0.63      0.82      0.71     14125
           1       0.51      0.28      0.36      9476
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

    accuracy                           0.60     23601
   macro avg       0.57      0.55      0.54     23601
weighted avg       0.58      0.60      0.57     23601
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
<<<<<<< HEAD
<<<<<<< HEAD
| K-Nearest Neighbors |  0.5777 |     0.5777 |
| Logistic Regression |  0.6268 |     0.6268 |
=======
| K-Nearest Neighbors |  0.5756 |     0.5756 |
| Logistic Regression |  0.6241 |     0.6241 |
| Linear SVC          |  0.6246 |     0.6246 |
| Random Forest       |  0.6110 |     0.6073 |
| Adaptive boosting   |  0.6335 |     0.6335 |
| Decision Tree       |  0.5702 |     0.5604 |
| Extra Trees         |  0.6016 |     0.6031 |
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
| K-Nearest Neighbors |  0.5805 |     0.5805 |
| Logistic Regression |  0.6238 |     0.6238 |
| Linear SVC          |  0.6231 |     0.6231 |
| Random Forest       |  0.6093 |     0.6065 |
| Adaptive boosting   |  0.6323 |     0.6323 |
| Decision Tree       |  0.5600 |     0.5575 |
| Extra Trees         |  0.6032 |     0.6032 |
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d
+---------------------+---------+------------+

<<<<<<< HEAD <<<<<<< HEAD >>>>>> 98d343efacd2506462a77b07e8ea11626b539aee =======

<<<<<<< HEAD

Model 3: Linear SVC

=======

Model 8: Naive Bayes Classifier

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD <<<<<<< HEAD

## Creating model
lsvc  = LinearSVC( dual = False)
lsvc.fit(x_train, y_train)

cv_score = cross_val_score(lsvc, x_train, y_train, cv = 10)
print(cv_score.mean())

## Computing predictions
y_pred = lsvc.predict(x_test)

## Evaluating the performance of the model
ac = accuracy_score(y_test,y_pred)
sc = lsvc.score(x_test,y_test)
cm = confusion_matrix(y_test, y_pred)

ax = sns.heatmap(cm, annot=True, fmt='g', cmap="Blues", linewidths=5, cbar=False)

=======

=======

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

     

## Creating model
gnb = GaussianNB()
gnb.fit(x_train, y_train)

## Calculating predictions
y_pred = gnb.predict(x_test)

## Evaluating the performance of our model
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = gnb.score(x_test,y_test)

ax = sns.heatmap(cm, annot=True, fmt='g', cmap="Blues", linewidths=5, cbar=False)  #annot=True to annotate cells, ftm='g' to disable scientific notation

# labels, title and ticks
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
ax.xaxis.set_label_position('top');
ax.xaxis.set_ticks_position('top');

ax.set_xlabel('Predicted labels', size = 20);ax.set_ylabel('True labels', size = 20); 
ax.set_title('Confusion Matrix', size = 30); 

ax.xaxis.set_ticklabels(['Not readmitted', 'Readmitted'])
ax.yaxis.set_ticklabels(['Not readmitted', 'Readmitted']);

print(classification_report(y_test, y_pred))

## Adding results to the dataframe
<<<<<<< HEAD
lsvc_sa = pd.DataFrame({'Model': ['Linear SVC'],'Score': [sc], 'Accuracy' : [ac]})
models_df2 = models_df2.append(lsvc_sa, ignore_index=True)

print(pdtabulate2(models_df2))
=======
knn_sa = pd.DataFrame({'Model': ['Naive Bayes'],'Score': [sc], 'Accuracy' : [ac]})
models_df2 = models_df2.append(knn_sa, ignore_index=True)

print(pdtabulate(models_df2))
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee


     

<<<<<<< HEAD

0.624392202582123
              precision    recall  f1-score   support

           0       0.63      0.92      0.75     14143
           1       0.61      0.19      0.29      9458

    accuracy                           0.63     23601
   macro avg       0.62      0.55      0.52     23601
weighted avg       0.62      0.63      0.56     23601
=======
              precision    recall  f1-score   support

           0       0.61      0.98      0.75     14125
           1       0.67      0.05      0.09      9476

    accuracy                           0.61     23601
<<<<<<< HEAD
   macro avg       0.63      0.51      0.40     23601
weighted avg       0.62      0.61      0.47     23601
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
   macro avg       0.64      0.52      0.42     23601
weighted avg       0.63      0.61      0.48     23601
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
<<<<<<< HEAD
<<<<<<< HEAD
| K-Nearest Neighbors |  0.5777 |     0.5777 |
| Logistic Regression |  0.6268 |     0.6268 |
| Linear SVC          |  0.6258 |     0.6258 |
=======
| K-Nearest Neighbors |  0.5756 |     0.5756 |
| Logistic Regression |  0.6241 |     0.6241 |
| Linear SVC          |  0.6246 |     0.6246 |
| Random Forest       |  0.6110 |     0.6073 |
| Adaptive boosting   |  0.6335 |     0.6335 |
| Decision Tree       |  0.5702 |     0.5604 |
| Extra Trees         |  0.6016 |     0.6031 |
| Naive Bayes         |  0.6074 |     0.6074 |
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
| K-Nearest Neighbors |  0.5805 |     0.5805 |
| Logistic Regression |  0.6238 |     0.6238 |
| Linear SVC          |  0.6231 |     0.6231 |
| Random Forest       |  0.6093 |     0.6065 |
| Adaptive boosting   |  0.6323 |     0.6323 |
| Decision Tree       |  0.5600 |     0.5575 |
| Extra Trees         |  0.6032 |     0.6032 |
| Naive Bayes         |  0.6077 |     0.6077 |
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d
+---------------------+---------+------------+

<<<<<<< HEAD <<<<<<< HEAD >>>>>> 98d343efacd2506462a77b07e8ea11626b539aee =======

<<<<<<< HEAD

Model 4: Random Forest

## Creating model
clf = RandomForestClassifier(n_estimators = 10)
clf = clf.fit(x_train, y_train)

scores = cross_val_score( clf, x_train, y_train, cv = 5)
scores.mean()

## Computing predictions
y_pred = clf.predict(x_test)

## Evaluating the model's performance
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = max(scores) # ???

## Adding results to the dataframe
rf_sa = pd.DataFrame({'Model': ['Random Forest'],'Score': [sc], 'Accuracy' : [ac]})
models_df2 = models_df2.append(rf_sa, ignore_index=True)

print(pdtabulate2(models_df2))

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
| K-Nearest Neighbors |  0.5777 |     0.5777 |
| Logistic Regression |  0.6268 |     0.6268 |
| Linear SVC          |  0.6258 |     0.6258 |
| Random Forest       |  0.6113 |     0.6081 |
+---------------------+---------+------------+

Model 5: Adaptive boosting

=======

The best score is obtained with the adaptive boosting model. Let's try other hyperparameters. Maybe we can improve the performance of the model.

Tuning models

Again, let's try to find the best hyperparameters using GridSearch

Grid Search with Adaptative Boosting

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD <<<<<<< HEAD

## Creating model
ab = AdaBoostClassifier()
ab.fit(x_train, y_train)

## Computing predictions
y_pred = ab.predict(x_test)

## Evaluating the model's performance
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = ab.score(x_test,y_test)

## Adding results to the dataframe
rf_sa = pd.DataFrame({'Model': ['Adaptive boosting'],'Score': [sc], 'Accuracy' : [ac]})
models_df2 = models_df2.append(rf_sa, ignore_index=True)

print(pdtabulate2(models_df2))
=======

=======

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

     

parameters = { 'n_estimators': [100,200,300],
               'learning_rate': [0.95, 0.98, 1.0],
               'algorithm': ['SAMME', 'SAMME.R']}
grid = GridSearchCV(AdaBoostClassifier(), parameters, n_jobs = -1, cv = 3)
grid.fit(x_train,y_train)
y_pred = grid.predict(x_test)
ac = accuracy_score(y_test, y_pred)
sc = grid.best_score_
print(grid.best_estimator_, grid.best_params_)
print(sc, ac)
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee


     

<<<<<<< HEAD <<<<<<< HEAD

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
| K-Nearest Neighbors |  0.5777 |     0.5777 |
| Logistic Regression |  0.6268 |     0.6268 |
| Linear SVC          |  0.6258 |     0.6258 |
| Random Forest       |  0.6113 |     0.6081 |
| Adaptive boosting   |  0.6361 |     0.6361 |
+---------------------+---------+------------+
=======
AdaBoostClassifier(learning_rate=0.98, n_estimators=300) {'algorithm': 'SAMME.R', 'learning_rate': 0.98, 'n_estimators': 300}
0.638165992003149 0.6351425787042921
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
AdaBoostClassifier(n_estimators=100) {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 100}
0.6365589556343271 0.6351002076183213
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

<<<<<<< HEAD

Model 6: Decision Tree

## Creating model
clf = DecisionTreeClassifier(max_depth=None, min_samples_split=2, random_state=0)
scores = cross_val_score(clf, x_train, y_train, cv=5)
scores.mean()
clf.fit( x_train, y_train)

## Computing predictions
y_pred = clf.predict(x_test)

## Evaluating the model's performance
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = max(scores) # ???

ax = sns.heatmap(cm, annot=True, fmt='g', cmap="Blues", linewidths=5, cbar=False)  #annot=True to annotate cells, ftm='g' to disable scientific notation

# labels, title and ticks
ax.xaxis.set_label_position('top');
ax.xaxis.set_ticks_position('top');

ax.set_xlabel('Predicted labels', size = 20);ax.set_ylabel('True labels', size = 20); 
ax.set_title('Confusion Matrix', size = 30); 

ax.xaxis.set_ticklabels(['Not readmitted', 'Readmitted'])
ax.yaxis.set_ticklabels(['Not readmitted', 'Readmitted']);

## Adding results to the dataframe
dt_sa = pd.DataFrame({'Model': ['Decision Tree'],'Score': [sc], 'Accuracy' : [ac]})
models_df2 = models_df2.append(dt_sa, ignore_index=True)

print(pdtabulate2(models_df2))
=======
The best parameters to use with the AdaBoostClassifier are n_estimators = 300, learning_rate = 0.98. The score of the model is a little bit improved but it's not enormous.

We can update the dataframe with the new values

models_df2.loc[4] = ['Adaptive boosting', sc, ac]

Grid Search with Random Forest

from sklearn.model_selection import GridSearchCV

parameters = {"n_estimators"      : [10,20,50, 100],
              "max_features"      : ["auto", "sqrt", "log2"],
              "min_samples_split" : [2,4,8],
              "bootstrap"         :[True, False],
              }

grid = GridSearchCV(RandomForestClassifier(), parameters, n_jobs = -1, cv = 3)
grid.fit(x_train,y_train)
y_pred = grid.predict(x_test)
ac = accuracy_score(y_test, y_pred)
sc = grid.best_score_
print(grid.best_estimator_, grid.best_params_)
print(sc, ac)
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

<<<<<<< HEAD <<<<<<< HEAD

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
| K-Nearest Neighbors |  0.5777 |     0.5777 |
| Logistic Regression |  0.6268 |     0.6268 |
| Linear SVC          |  0.6258 |     0.6258 |
| Random Forest       |  0.6113 |     0.6081 |
| Adaptive boosting   |  0.6361 |     0.6361 |
| Decision Tree       |  0.5705 |     0.5572 |
+---------------------+---------+------------+
=======
RandomForestClassifier(max_features='sqrt', min_samples_split=8) {'bootstrap': True, 'max_features': 'sqrt', 'min_samples_split': 8, 'n_estimators': 100}
0.6360790796130545 0.6347612389305538
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
RandomForestClassifier(max_features='log2', min_samples_split=8) {'bootstrap': True, 'max_features': 'log2', 'min_samples_split': 8, 'n_estimators': 100}
0.6358703392469826 0.6359900004237109
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

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=======

We can update the dataframe with the new values

models_df2.loc[3] = ['Random Forest', sc, ac]

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

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Model 7: ExtraTrees

=======

Grid Search with Naives Bayes

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

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##Creating model 
clf = ExtraTreesClassifier(n_estimators=10, max_depth=None, min_samples_split=2, random_state=0)
scores = cross_val_score(clf, x_train, y_train, cv=5)
scores.mean()
clf.fit(x_train, y_train)

## Computing predictions
y_pred = clf.predict(x_test)

## Evaluating the model's performance
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = max(scores) # ???

ax = sns.heatmap(cm, annot=True, fmt='g', cmap="Blues", linewidths=5, cbar=False)  #annot=True to annotate cells, ftm='g' to disable scientific notation

# labels, title and ticks
ax.xaxis.set_label_position('top');
ax.xaxis.set_ticks_position('top');

ax.set_xlabel('Predicted labels', size = 20);ax.set_ylabel('True labels', size = 20); 
ax.set_title('Confusion Matrix', size = 30); 

ax.xaxis.set_ticklabels(['Not readmitted', 'Readmitted'])
ax.yaxis.set_ticklabels(['Not readmitted', 'Readmitted']);

print(classification_report(y_test, y_pred))

## Adding results to the dataframe
et_sa = pd.DataFrame({'Model': ['Extra Trees'],'Score': [sc], 'Accuracy' : [ac]})
models_df2 = models_df2.append(et_sa, ignore_index=True)

print(pdtabulate(models_df2))
=======

=======

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

     

nb_classifier = GaussianNB()

params_NB = {'var_smoothing': np.logspace(0,-9, num=100)}
gs_NB = GridSearchCV(estimator=nb_classifier, 
                 param_grid=params_NB, 
                 cv=5,   # use any cross validation technique 
                 verbose=1, 
                 scoring='accuracy') 
gs_NB.fit(x_train, y_train)
y_pred = grid.predict(x_test)
ac = accuracy_score(y_test, y_pred)
sc = grid.best_score_
print(grid.best_estimator_, grid.best_params_)
print(sc, ac)
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee


     

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              precision    recall  f1-score   support

           0       0.63      0.81      0.71     14143
           1       0.49      0.28      0.35      9458

    accuracy                           0.60     23601
   macro avg       0.56      0.54      0.53     23601
weighted avg       0.57      0.60      0.57     23601

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
| K-Nearest Neighbors |  0.5777 |     0.5777 |
| Logistic Regression |  0.6268 |     0.6268 |
| Linear SVC          |  0.6258 |     0.6258 |
| Random Forest       |  0.6113 |     0.6081 |
| Adaptive boosting   |  0.6361 |     0.6361 |
| Decision Tree       |  0.5705 |     0.5572 |
| Extra Trees         |  0.6091 |     0.5964 |
+---------------------+---------+------------+
=======
Fitting 5 folds for each of 100 candidates, totalling 500 fits
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RandomForestClassifier(max_features='sqrt', min_samples_split=8) {'bootstrap': True, 'max_features': 'sqrt', 'min_samples_split': 8, 'n_estimators': 100}
0.6360790796130545 0.6347612389305538
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
RandomForestClassifier(max_features='log2', min_samples_split=8) {'bootstrap': True, 'max_features': 'log2', 'min_samples_split': 8, 'n_estimators': 100}
0.6358703392469826 0.6359900004237109
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

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=======

We can update the dataframe with the new values

models_df2.loc[7] = ['Naive Bayes', sc, ac]

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

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Model 8: Naive Bayes Classifier

=======

Grid Search with Extra Trees

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## Creating model
gnb = GaussianNB()
gnb.fit(x_train, y_train)

## Calculating predictions
y_pred = gnb.predict(x_test)

## Evaluating the performance of our model
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)
sc = gnb.score(x_test,y_test)

ax = sns.heatmap(cm, annot=True, fmt='g', cmap="Blues", linewidths=5, cbar=False)  #annot=True to annotate cells, ftm='g' to disable scientific notation

# labels, title and ticks
ax.xaxis.set_label_position('top');
ax.xaxis.set_ticks_position('top');

ax.set_xlabel('Predicted labels', size = 20);ax.set_ylabel('True labels', size = 20); 
ax.set_title('Confusion Matrix', size = 30); 

ax.xaxis.set_ticklabels(['Not readmitted', 'Readmitted'])
ax.yaxis.set_ticklabels(['Not readmitted', 'Readmitted']);

print(classification_report(y_test, y_pred))

## Adding results to the dataframe
knn_sa = pd.DataFrame({'Model': ['Naive Bayes'],'Score': [sc], 'Accuracy' : [ac]})
models_df2 = models_df2.append(knn_sa, ignore_index=True)

print(pdtabulate(models_df2))
=======

=======

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

     

from sklearn.model_selection import GridSearchCV

parameters = {"n_estimators"      : [10,20,50,100]}

grid = GridSearchCV(ExtraTreesClassifier(), parameters, n_jobs = -1, cv = 3)
grid.fit(x_train,y_train)
y_pred = grid.predict(x_test)
ac = accuracy_score(y_test, y_pred)
sc = grid.best_score_
print(grid.best_estimator_, grid.best_params_)
print(sc, ac)
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee


     

<<<<<<< HEAD

              precision    recall  f1-score   support

           0       0.60      0.99      0.75     14143
           1       0.70      0.02      0.05      9458

    accuracy                           0.60     23601
   macro avg       0.65      0.51      0.40     23601
weighted avg       0.64      0.60      0.47     23601

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
| K-Nearest Neighbors |  0.5777 |     0.5777 |
| Logistic Regression |  0.6268 |     0.6268 |
| Linear SVC          |  0.6258 |     0.6258 |
| Random Forest       |  0.6113 |     0.6081 |
| Adaptive boosting   |  0.6361 |     0.6361 |
| Decision Tree       |  0.5705 |     0.5572 |
| Extra Trees         |  0.6091 |     0.5964 |
| Naive Bayes         |  0.6048 |     0.6048 |
+---------------------+---------+------------+
=======
ExtraTreesClassifier() {'n_estimators': 100}
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0.6220130830907383 0.6241684674378204
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
=======
0.6229730846883087 0.6201432142705817
>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

<<<<<<< HEAD

=======

We can update the dataframe with the new values

models_df2.loc[6] = ['Extra Trees', sc, ac]

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

Comparing models

Let's compare all different models we tried.

Performance of each model

print(pdtabulate(models_df2.sort_values(by=['Score', 'Accuracy'], ascending = False)))

+---------------------+---------+------------+
| Model               |   Score |   Accuracy |
|---------------------+---------+------------|
| Adaptive boosting   |  0.6366 |     0.6351 |
| Random Forest       |  0.6359 |     0.6360 |
| Naive Bayes         |  0.6359 |     0.6360 |
| Logistic Regression |  0.6238 |     0.6238 |
| Linear SVC          |  0.6231 |     0.6231 |
| Extra Trees         |  0.6230 |     0.6201 |
| K-Nearest Neighbors |  0.5805 |     0.5805 |
| Decision Tree       |  0.5600 |     0.5575 |
+---------------------+---------+------------+

Plot

<<<<<<< HEAD <<<<<<< HEAD

bp = models_df[['Model', 'Accuracy']].groupby('Model').sum().plot_bokeh(kind = 'scatter',legend = False, title = 'Accuracy by model', line_cap = 'round',line_dash = 'dotted', show_figure = False)
=======

=======

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

     

bp = models_df2[['Model', 'Accuracy']].groupby('Model').sum().plot_bokeh(kind = 'scatter',legend = False, title = 'Accuracy by model', line_cap = 'round',line_dash = 'dotted', show_figure = False)
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
bp.xaxis.major_label_orientation = np.pi/6
show(bp)


     

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=======

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee =======

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

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Barplot

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from bokeh.models import Range1d
from bokeh.models import Span

<<<<<<< HEAD
bp = models_df.groupby('Model').sum().plot_bokeh(kind = 'barh', title = 'Accuracy and score by model',show_figure = False)
=======
bp = models_df2.groupby('Model').sum().plot_bokeh(kind = 'barh', title = 'Accuracy and score by model',show_figure = False)
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee
bp.x_range = Range1d(-0.1,1.4)

meanAc = Span(location = models_df.Accuracy.mean(), dimension = 'height', line_color = 'crimson', line_width = 1)
meanSc = Span(location = models_df.Score.mean(), dimension = 'height', line_color = 'blue', line_width = 1)
limit = Span(location = 1, dimension = 'height', line_color = 'white', line_width = 3)

bp.renderers.extend([limit, meanAc, meanSc])
show(bp)

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=======

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee =======

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

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<<<<<<< HEAD =======

---

Conclusion

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee

Remarks about cases

Let's summarize what we have done yet.
We made two different hypothesis concerning our machine learning predictions:

• 1. Predict if patients will be readmitted under 30 days
• 1. Predict if patients will be readmitted (ie under or over 30 days)
     

Each hypothesis led to different results due to the different proportions of values.
Now that we run some models on both, we can compare them.

First, we can make pie charts to compare both cases and models

fig = make_subplots(cols = 8,
                    rows = 2,
                    row_titles = ['    Case 1', '    Case 2'],
                    subplot_titles = 2 * [x for x in models_df.Model],
                    specs = [8*[{'type': 'domain'}], 8*[{'type': 'domain'}]])

for i in range(8):
    fig.add_traces(
        go.Pie(labels=['sucess', 'fail'],
               values=[models_df.loc[i][2], 1 - models_df.loc[i][2]],
               hole=0.3,
               name=str(i)), 1, i + 1)
    fig.add_traces(
        go.Pie(labels=['sucess', 'fail'],
               values=[models_df2.loc[i][2], 1 - models_df2.loc[i][2]],
               hole=0.3,
               name=str(i)), 2, i + 1)

fig.layout.template = 'plotly_dark'
fig.update_layout(showlegend=False)
for annotation in fig['layout']['annotations']:
    annotation['textangle'] = -20
fig.show()

Let's concatenate both dataframe to have a holistic view of our results.

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summary_ML = pd.concat([models_df, models_df2],
                               axis = 1, 
                               keys=['Readmission under 30 days', 'Readmission under or over 30 days'])

Now that we have a dataframe containing all reults from our models, we can save it to a csv file.

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summary_ML.to_csv('summary_ML.csv', index=False, sep = ';')
=======

=======

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

     

summary_ML.to_csv(r'..\Dataset\summary_ML.csv', index=False, sep = ';')
>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee


     

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#headerstyle = {'selector': 'tr', 'props': [('background-color', 'lightblue') ,('border','1.5px solid black')]}
#rowstyle = {'selector': 'td', 'props': [('background-color', 'white') ,('border','1.5px solid black')]}

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summary_ML.style.hide_index().highlight_max(color = 'lightgreen').highlight_min(color = 'red')

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======= #T_a2331_row2_col1,#T_a2331_row3_col2,#T_a2331_row4_col2,#T_a2331_row4_col4,#T_a2331_row4_col5{ ======= #T_f7f3e_row2_col1,#T_f7f3e_row2_col2,#T_f7f3e_row3_col5,#T_f7f3e_row4_col4,#T_f7f3e_row7_col5{ >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d background-color: lightgreen; }#T_f7f3e_row5_col1,#T_f7f3e_row5_col2,#T_f7f3e_row5_col4,#T_f7f3e_row5_col5{ background-color: red; }

Readmission under 30 days			Readmission under or over 30 days
Model	Score	Accuracy	Model	Score	Accuracy
K-Nearest Neighbors	0.907801	0.907801	K-Nearest Neighbors	0.577730	0.577730
Logistic Regression	0.912716	0.912716	Logistic Regression	0.626795	0.626795
Linear SVC	0.913266	0.913266	Linear SVC	0.625779	0.625779
Random Forest	0.910884	0.912673	Random Forest	0.611291	0.608110
Adaptive boosting	0.912673	0.912673	Adaptive boosting	0.636117	0.636117
Decision Tree	0.831490	0.826575	Decision Tree	0.570534	0.557180
Extra Trees	0.910675	0.912080	Extra Trees	0.609099	0.596373
Naive Bayes	0.088810	0.088810	Naive Bayes	0.604847	0.604847

<<<<<<< HEAD >>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee ======= >>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

Readmission under 30 days			Readmission under or over 30 days
Model	Score	Accuracy	Model	Score	Accuracy
K-Nearest Neighbors	0.906699	0.906699	K-Nearest Neighbors	0.580526	0.580526
Logistic Regression	0.912843	0.912843	Logistic Regression	0.623829	0.623829
Linear SVC	0.912970	0.912970	Linear SVC	0.623109	0.623109
Random Forest	0.911660	0.912758	Random Forest	0.635870	0.635990
Adaptive boosting	0.911576	0.912885	Adaptive boosting	0.636559	0.635100
Decision Tree	0.834203	0.832719	Decision Tree	0.559996	0.557476
Extra Trees	0.911493	0.912504	Extra Trees	0.622973	0.620143
Naive Bayes	0.885302	0.881403	Naive Bayes	0.636079	0.634761	Naive Bayes	0.886533	0.878310	Naive Bayes	0.635870	0.635990

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summary_ML['Readmission under 30 days'].Accuracy.mean(), summary_ML['Readmission under or over 30 days'].Accuracy.mean()

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<<<<<<< HEAD <<<<<<< HEAD

(0.7983242235498494, 0.6041163510020763)

=======

(0.8974196008643701, 0.6141900766916655)

>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee =======

(0.8977109020804204, 0.614020592347782)

>>>>>>> 007713fd3640aec1391b4d043eaf112c8acff68d

We can see that the differents models perform better in case 1 but as mentionned earlier it is not surprising because the two categories to predict are too unbalanced.

In case 2, we lost some accuracy but it is a much more realistic modelization

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Conclusion

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Drafts

count_0 = 0 count_1 = 1 for rows in y_pred : if rows == 0 : count_0 += 1 else : count_1 += 1 print(count_0, count_1)#now it's time to visualize some elements of our model ks = [x for x in np.arange(2,10, 1)] acs = [] for k in ks: print(k) knn = KNeighborsClassifier(n_neighbors = k).fit(x_train, y_train) y_pred = knn.predict(x_test) ac = accuracy_score(y_test,y_pred) acs.append(ac) plt.plot(ks,acs,color = 'blue',linestyle='dashed', marker='o',markerfacecolor='red', markersize=10)#List Hyperparameters that we want to tune. leaf_size = list(range(1,50)) n_neighbors = list(range(1,30)) p=[1,2] #Convert to dictionary hyperparameters = dict(leaf_size=leaf_size, n_neighbors=n_neighbors, p=p) #Create new KNN object knn_2 = KNeighborsClassifier() #Use GridSearch from sklearn.model_selection import GridSearchCV clf = GridSearchCV(knn_2, hyperparameters, cv=10) #Fit the model best_model = clf.fit(x,y) #Print The value of best Hyperparameters print('Best leaf_size:', best_model.best_estimator_.get_params()['leaf_size']) print('Best p:', best_model.best_estimator_.get_params()['p']) print('Best n_neighbors:', best_model.best_estimator_.get_params()['n_neighbors'])from sklearn.model_selection import GridSearchCV parameters = { 'leaf_size' : [1, 5, 10] } grid = GridSearchCV(KNeighborsClassifier, parameters, n_jobs = -1, cv = 5) grid.fit(x_train,y_train) print(grid.best_score_, grid.best_estimator_)

Principal Component Analysis

from sklearn.decomposition import PCA pca = PCA() x_train_pca = pca.fit_transform(x_train) x_test_pca = pca.transform(x_test)colonnes = list(diabetes_df_ml.columns) colonnes.remove('readmitted')# number of components n_pcs= pca.components_.shape[0] # get the index of the most important feature on EACH component i.e. largest absolute value # using LIST COMPREHENSION HERE most_important = [np.abs(pca.components_[i]).argmax() for i in range(n_pcs)] initial_feature_names = colonnes # get the names most_important_names = [initial_feature_names[most_important[i]] for i in range(n_pcs)] # using LIST COMPREHENSION HERE AGAIN dic = {'PC{}'.format(i+1): most_important_names[i] for i in range(n_pcs)} # build the dataframe df = pd.DataFrame(sorted(dic.items(), reverse = True))dfcolors = ['red' if x == 1 else 'blue' for x in y_train] #markers = ['.' if x == 1 else '.' for x in y_train] pd.DataFrame(x_train_pca).plot(kind = 'scatter', x = 1, y = 0, c = colors, marker = ".",s = 2, figsize = (20,20)) =======

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>>>>>>> 98d343efacd2506462a77b07e8ea11626b539aee